People management

Meta’s Manager Behaviors

  • Show care/empathy by understanding what is most important to people.
  • Match people’s strengths and skills to impactful roles and experiences.
  • Give and seek timely feedback and ask the same of your team.
  • Recognize people for their contributions.
  • Set clear expectations for people and teams.
  • Empower people to execute against their priorities.
  • Communicate context from the company and organization.
  • Build a culture where differences are appreciated and everyone feels valued.
  • Help people build and sustain productive relationships.
  • Collaborate with partners on shared goals and priorities.

Examples

Hiring

  • Hiring people for an engineering team is like building a soccer team. You need a mix of players with different skills and specialties to perform well on the field. Just as you need strikers, defenders, midfielders, and goalkeepers in soccer, in engineering, you need coders, architects, testers, and system administrators, among others.
  • A star striker alone can’t win the game without defenders ensuring the opposition doesn’t score, and a strong defense without a creative midfield won’t make plays happen. Similarly, in an engineering team, a top coder alone won’t succeed without people ensuring the infrastructure is solid, the code quality is high, and the product is tested properly.
  • The best teams balance skills, experience, and chemistry. A great soccer team isn’t just about picking the best individual players; it’s about how well they collaborate and play off each other’s strengths. In engineering, it’s about finding not just the most talented engineers but also those who can communicate well, support each other, and work together toward the team’s goals.
  • Like a soccer coach, as a manager, you look for both technical skill and the ability to work within the team’s strategy.

Recruiter - ppl management (Detail, Managing through managers, Reflection)

  • 1 People management. The Purpose of the people management interview is to assess your philosophy, process, and strategy around your management experience as well as how you communicate effectively to your team. ◦ During this interview you will be asked to tell a lot of stories about relevant experiences. Be prepared with a few stories highlighting your people management, coaching/mentorship, building a team, success hiring and firing, examples of managing strong performers and low performers (how did you help turn situations around).  ◦ Describe a work experience that you consider to be most interesting, challenging, and perhaps relevant to what you view as the opportunity at Facebook. ◦ Describe a situation where you worked cross-functionally to remove a significant barrier for your team, or a situation in which you were able to really maximize the productivity of a team by digging into the inner workings and dynamics of the team.

  • People Leadership:
  • Team Structure:
    • How many people are on your team?
    • What is your team working on?
  • Performance Management:
    • Have you had to manage performance issues?
    • What is your process for performance management?
    • Have you managed through managers?
    • How do you manage through managers to ensure alignment and accountability?
    • How do you grow leaders within your team?
    • If someone didn’t meet expectations, what actions did you take to help them improve?
    • Have you had to hire or fire based on performance?
  • Philosophy on Promotions:
    • What is your philosophy on promotion?
    • How do you grow individuals who aren’t quite ready for promotion yet?
  • Team Retention and Culture:
  • Retention and Satisfaction:
    • How do you ensure team retention and satisfaction?
    • How do you keep the team motivated?
  • Team Building:
    • Have you organized offsite events for your team? How have you done them?
    • What are you focused on in growing and fostering a positive culture within your team?
  • Meetings and Feedback:
  • Team Meetings:
    • What is the structure of your team meetings?
    • How do you conduct 1:1 conversations?
  • Feedback:
    • How do you give feedback to your team members?
    • Do you ask for feedback from your reports?
  • Building Teams:
  • Team Structure and Reporting:
    • How are people structured in your team? Who reports up to you?
  • Recruitment:
    • Are you focused on technical performance, or do you balance technical skills with culture and other factors?
    • How do you partner with recruiting to build diversity within your team?

  • Your senior engineer doesn’t have enough context to make this decision? Talk about how you gave it to them.

  • Your mid-level engineer doesn’t have the skills yet. Do they have a mentor in that area?

Rubric - ppl management

  • Growing reports: different growth plans per level
  • What is your management type
  • How do you grow folks into leaders
  • Low performers
  • How to hire
  • how to change current culture
  • how to reward employees
  • biggest mistake

Retrospective (Ownership, Had failures, Scope, influence managers, influence overall vision)

  • Rufus : chatbot

1 point 3 acres

  • I didn’t mention it. I think I might have failed in the project retro. I talked about some of the LightGBM NN models I worked on. The interviewer didn’t seem interested and didn’t mention any tech follow-up. He just said that they don’t use NN models much and that they are not very interpretable and are only suitable for situations with large sample sizes. The logistic model is enough to handle most of the RDS work.
  • Project Retrospective: just chat with the hiring manager, answer some behavioral questions about leadership and cross-functional cooperation, and introduce the project I am most proud of.
  • There’s no fixed list. It will come from your project and you should be able to show that you know how things work, why some decisions were made, etc.

  • More focused on the technical side of it. Some decisions might have been organisational and being able to explain the context is also important.

  • You’ll also go through a project retrospective round—also called a technical deep dive. The project retrospective is a conversational interview where you’ll have an in-depth discussion about a technical project that you directly worked on in the past. Typically you’ll be told beforehand to prepare for a project discussion. The question may be framed simply as “Tell me about a project you’re proud of,” or “Talk about a project you worked on recently.” Facebook has recently asked this way: “Describe the most technically complex project that you have worked on and why it was complex.”

  • Much like the system design interview, these project discussions may cover the requirements, features, and technical tradeoffs you made in the project, and could optionally involve whiteboarding or diagrams to explain how the system works. Since you’re discussing a project that you worked on, however, you’re expected to go more in depth on the technical discussion, explain the role you served in the project, and discuss the final outcome or impact the project had on the company.

  • As an engineering manager, you may also be asked to discuss the people problems that arose on the team during the project, how you resolved them, and what you would do differently in the future. You can expect lots of follow-up questions.

  • To prepare for project retrospectives, we recommend creating a story bank, choosing a few complex, recent projects to explore more deeply, writing out all technical details, decisions, and people management issues. Be sure to include trade-offs and cross-functional communication hurdles. When you’re finished writing, take these experiences and put them into the STAR format so that you can communicate succinctly in the real interview.

Rubric - Retro

  • Key value prop, whats the business or product?
  • Vision / Strategy:
    • Inclusivity - have my reports/ PMs/ stakeholders all aggregate ideas in project in a quip doc. Run RICE framework against the OKRs and KPIs to choose projects
  • Metrics / Goals (cost metrics, feasibility of alternates)
    • How do they correlate back to the business metrics
    • These are more actionable
  • Roadmap (your contribution specifically)
    • How to make one
    • How to identify dependencies
    • How to work in other disciplines
    • cross collab (how to influence other teams)
  • Delivery
    • tracking
    • slipping deadlines
    • bringing back a failed delivery
  • Introspection/ Reflection
    • Why didn’t you do these already in the project

Recruiter - Retro

  • You’ll be sharing more about a project you’ve led walking us through the start to finish and key project highlights. Specifically, be prepared to discuss: The Business understanding, product/project planning. How did you negotiate with stakeholders and peers? Who was involved in the project? How did you go about driving success? How did you keep track of progress? Your understanding of design tradeoffs, business vs operations vs development considerations. How do you scale systems and business processes?  
  • Project Retro:
    • Have a couple of solid projects ready.
    • Focus on project leadership.
    • Think back on a time when you delivered a project.
  • Situational-Based Example:
    • A product you led that changed halfway through.
    • Delivered with little to no direction from leadership.
  • Project Details:
    • Explain the transition from the initial goal to success metrics.
    • Discuss the roadblocks, challenges, and tradeoffs.
    • Describe why you chose option A vs. option B.
    • Could you have done something differently?
  • Team and Task Management:
    • How did you remain on task?
    • How did you keep the team on task?
    • How did you drive deliverables and execution?
  • Project Impact:
    • Why is it a meaningful project?
    • Highlight the tech impact and product impact.
    • How did it shape you as a leader?
  • Reflection:
    • Avoid discussing any project that failed.
    • Reflect on the project and what you learned.

Behavioral

Recruiter - behavioral

  • Practice solid answers for the following questions: • How do you deal with conflict? • What were some excellent collaborations you’ve had? • Can you tell me about four people whose careers you have fundamentally improved? • Describe a few of your peers at your company and what type of relationship you have with each of them. • What did you do on your very best day at work? • What does office politics mean to you, and do you see politics as your job? • Tell me about a project that you led that failed. Why did it fail and what did you learn?

  • Behavioral:

    • Career Path:
      • Why did you choose this path in your career?
      • Why Meta?
    • Navigating the Unknown:
      • How do you navigate undefined or vague requirements?
      • How do you handle working without a clear roadmap every day?
      • How have you delivered in similar situations in your prior roles?
    • Leadership and Influence:
      • How do you pitch ideas to leadership?
      • How do you get buy-in from cross-functional teams (XFN)?
      • How have you secured political buy-in in the past?
    • Conflict Resolution:
      • How do you resolve conflict situations or disagreements in the workplace?
    • Self-Awareness:
      • Do you know your strengths and weaknesses?
      • What can you improve, and why?
      • How do you counterbalance your weaknesses with positive behaviors?
    • Feedback:
      • How have you given feedback to peers?
      • How have you received feedback from peers?
      • What changes resulted from that feedback? Did the individual change? Did you change?

Personal Achievements & Leadership:

  • What project are you most proud of?
  • Tell me about a time when you took the initiative.
  • Describe a time when you started collaboration across teams.
  • What is your role in the team/project you lead?
  • Describe a time when you had to manage competing priorities.
  • How do you maintain the team’s project priorities and help resolve priority conflicts?

1. What project are you most proud of?

Situation: At Amazon AWS, I led a project aimed at enhancing our customer support AI by developing a sophisticated Multimodal Retrieval-Augmented Generation (RAG) pipeline. The goal was to integrate text, image, and voice data to provide more accurate and contextually relevant responses to customer inquiries.

Task: My responsibility was to design and implement an end-to-end multimodal RAG pipeline that could seamlessly process and integrate multiple data modalities, ensuring scalability, low latency, and high accuracy in generating responses. This involved coordinating across various teams, including data engineering, machine learning, and product management.

Action:

  1. Pipeline Architecture Design:
    • Data Ingestion: Developed a modular ingestion framework using AWS Lambda for text preprocessing, Amazon Rekognition for image feature extraction, and Amazon Transcribe for voice-to-text conversion. This ensured real-time processing of incoming data streams.
    • Feature Encoding: Utilized BERT for text embeddings, ResNet-50 for image features, and Wav2Vec for encoding voice data. Standardized these embeddings into a unified vector space to facilitate cross-modal interactions.
    • Fusion Mechanism: Implemented a cross-modal attention mechanism within a transformer architecture. This involved creating custom attention layers that dynamically weighted the importance of each modality based on the query context.
    • Retrieval System: Built a scalable vector search system using Amazon Elasticsearch Service, optimized for high-dimensional vectors. This system enabled rapid retrieval of relevant documents across text, image, and voice datasets.
    • Generation Component: Integrated a fine-tuned GPT-4 model using few-shot prompting to generate coherent and contextually appropriate responses. Employed reinforcement learning from human feedback (RLHF) to iteratively improve response quality.
    • Personalization Layer: Incorporated user-specific data through Amazon Personalize to tailor responses based on historical interactions and preferences, enhancing the relevance and personalization of support.
  2. Implementation:
    • Model Training and Deployment: Leveraged AWS SageMaker for training the multimodal models, ensuring efficient resource utilization and scalability. Deployed the models using SageMaker endpoints with auto-scaling configurations to handle variable traffic loads.
    • CI/CD Pipeline: Established a robust CI/CD pipeline using AWS CodePipeline and CodeBuild, enabling automated testing, deployment, and monitoring of pipeline updates. This reduced deployment times and minimized downtime.
    • Data Privacy and Compliance: Implemented stringent data encryption both in transit and at rest using AWS KMS. Ensured compliance with GDPR and other relevant regulations by incorporating data anonymization and access controls.
  3. Testing and Optimization:
    • A/B Testing: Conducted extensive A/B testing to evaluate different model configurations and fusion strategies. Analyzed performance metrics such as response accuracy, latency, and user satisfaction to identify optimal settings.
    • Hyperparameter Tuning: Applied hyperparameter optimization techniques using SageMaker’s Hyperparameter Tuning Jobs to enhance model performance. Reduced inference latency by 20% through model pruning and quantization without compromising accuracy.
    • Scalability Enhancements: Optimized the retrieval system for horizontal scalability, ensuring it could handle increased data volumes and concurrent queries without degradation in performance.

Result: The multimodal RAG pipeline achieved a 30% reduction in average response time and a 25% increase in customer satisfaction scores. The system successfully handled real-time integration of text, image, and voice data, leading to more accurate and personalized support interactions. Additionally, the modular design facilitated a 40% increase in adoption across different departments within Amazon, demonstrating its scalability and versatility.

Reflection: This project underscored the critical importance of integrating diverse data modalities to enhance AI capabilities. I learned that meticulous pipeline design and robust cross-functional collaboration are essential for handling complex, real-world data scenarios. Additionally, leveraging AWS’s suite of services enabled us to build a scalable and efficient system. This experience reinforced my ability to lead technically challenging projects and adapt innovative solutions to meet evolving business needs.

2. Tell me about a time when you took the initiative.

Situation: While managing the Generative AI team at AWS, I identified a gap in our existing AI services: the lack of effective personalization in customer interactions. Despite having robust NLP capabilities, our systems weren’t leveraging user-specific data to tailor responses, leading to generic interactions that didn’t fully meet customer needs.

Task: I took the initiative to propose and lead a project aimed at integrating personalized recommendation systems with our existing generative AI models. The objective was to enhance the relevance and effectiveness of AI-driven customer support by incorporating user history and preferences.

Action:

  1. Needs Assessment and Proposal:
    • Conducted a thorough analysis of customer interaction data to identify patterns and areas where personalization could add value.
    • Developed a comprehensive proposal outlining the benefits of integrating a recommender system with our generative AI models, including potential improvements in customer satisfaction and engagement.
  2. Stakeholder Buy-In:
    • Presented the proposal to senior leadership, highlighting the technical feasibility and projected ROI.
    • Secured approval and allocated a dedicated cross-functional team comprising data scientists, engineers, and product managers.
  3. System Design and Integration:
    • Data Pipeline Enhancement: Extended the existing data ingestion pipeline to include user-specific data from Amazon Personalize, ensuring real-time access to user history and preferences.
    • Model Integration: Developed a hybrid model architecture where the recommender system would pre-filter potential responses based on user data before passing them to the generative model for final response generation.
    • Personalization Algorithms: Implemented collaborative filtering and content-based filtering techniques within Amazon Personalize to generate personalized recommendations that informed the generative AI responses.
  4. Implementation and Deployment:
    • Utilized AWS SageMaker to train and deploy the integrated models, ensuring seamless scalability and low latency.
    • Established automated testing frameworks to validate the accuracy and relevance of personalized responses, employing both automated metrics and user feedback loops.
  5. Monitoring and Optimization:
    • Set up comprehensive monitoring using Amazon CloudWatch to track system performance and user engagement metrics.
    • Iteratively refined the personalization algorithms based on real-time feedback and performance data, employing A/B testing to validate improvements.

Result: The initiative resulted in a 35% increase in customer satisfaction scores and a 20% reduction in average handling time for support interactions. Personalized responses led to more meaningful and efficient customer engagements, enhancing overall user experience. Additionally, the project demonstrated significant improvements in AI model performance, driving a 15% increase in first-contact resolution rates.

Reflection: Taking the initiative to integrate personalization into our AI services highlighted the importance of proactively identifying and addressing gaps in existing systems. I learned that successful innovation often requires stepping beyond immediate responsibilities to drive strategic improvements. Additionally, fostering cross-functional collaboration and securing stakeholder buy-in are crucial for the successful implementation of complex projects. This experience reinforced my ability to lead transformative initiatives that deliver tangible business value.

3. Describe a time when you started collaboration across teams.

Situation: At Amazon AWS, our Generative AI organization was developing a new multimodal RAG pipeline. However, we encountered challenges in integrating voice data effectively due to limited expertise within our team. The voice data was crucial for enhancing our AI’s ability to handle customer support interactions that included voice inputs alongside text and images.

Task: I recognized the need for specialized knowledge in voice processing and decided to initiate collaboration with the Alexa AI team, where I previously managed query understanding and personalization projects. The objective was to leverage their expertise to enhance our multimodal RAG pipeline’s voice processing capabilities.

Action:

  1. Identifying Collaboration Opportunities:
    • Assessed the technical requirements for integrating voice data into the RAG pipeline.
    • Identified key areas where Alexa AI’s expertise in query understanding and personalization could add value, particularly in voice transcription accuracy and contextual understanding.
  2. Establishing Communication Channels:
    • Organized an initial meeting between the Generative AI team and the Alexa AI team to discuss project goals, challenges, and potential collaboration points.
    • Established a joint Slack channel and scheduled regular cross-team meetings to facilitate ongoing communication and knowledge sharing.
  3. Joint Development Efforts:
    • Voice Data Integration: Collaborated with Alexa AI engineers to integrate Amazon Transcribe’s advanced transcription models into our pipeline, enhancing the accuracy and reliability of voice data conversion to text.
    • Contextual Understanding: Worked with query understanding experts to develop context-aware processing modules that could interpret nuanced voice queries, improving the generative model’s ability to respond appropriately.
    • Personalization Integration: Leveraged Alexa AI’s personalization frameworks to incorporate user-specific voice interaction data, enabling more tailored and relevant responses in the RAG pipeline.
  4. Coordinated Testing and Optimization:
    • Conducted joint testing sessions to evaluate the integrated system’s performance, focusing on metrics such as transcription accuracy, response relevance, and latency.
    • Implemented iterative optimization cycles based on feedback from both teams, ensuring that voice data integration met the desired performance standards.
  5. Documentation and Knowledge Transfer:
    • Developed comprehensive documentation outlining the integrated pipeline architecture, best practices, and troubleshooting guidelines.
    • Facilitated knowledge transfer sessions to ensure both teams were well-versed in the integrated system’s functionalities and maintenance procedures.

Result: The collaboration resulted in a highly robust multimodal RAG pipeline capable of accurately processing and integrating voice, text, and image data. This led to a 25% improvement in response accuracy for voice-based customer interactions and a 20% reduction in processing latency. Additionally, the successful cross-team collaboration fostered a culture of knowledge sharing and set a precedent for future inter-departmental projects, enhancing overall organizational synergy.

Reflection: This experience highlighted the immense value of cross-functional collaboration in overcoming technical challenges and driving innovation. I learned that effective collaboration requires proactive communication, mutual respect for each team’s expertise, and a shared commitment to common goals. By leveraging the strengths of different teams, we were able to build a more comprehensive and effective solution than either team could have achieved independently. This reinforced my belief in the power of collaborative leadership and the importance of fostering strong inter-team relationships.

4. What is your role in the team/project you lead?

Situation: As the manager of the Generative AI organization at Amazon AWS, I was tasked with leading the development of a cutting-edge Multimodal RAG pipeline aimed at enhancing our AI services with integrated text, image, and voice capabilities for customer support.

Task: My role encompassed overseeing the entire project lifecycle, from initial conception and design through to implementation, deployment, and continuous improvement. This required not only technical leadership but also strategic planning, team coordination, and stakeholder management.

Action:

  1. Strategic Planning and Vision Setting:
    • Defined the project’s objectives, scope, and success metrics in alignment with AWS’s broader AI strategy.
    • Developed a detailed project roadmap outlining key milestones, deliverables, and timelines, ensuring alignment with business goals and resource availability.
  2. Team Leadership and Coordination:
    • Assembled a cross-functional team comprising data scientists, machine learning engineers, software developers, and UX designers.
    • Conducted regular team meetings to monitor progress, address challenges, and ensure that all team members were aligned with the project’s goals.
    • Fostered a collaborative environment by encouraging knowledge sharing and continuous learning, facilitating workshops on advanced multimodal AI techniques and best practices.
  3. Technical Oversight and Guidance:
    • Provided technical direction on the architecture and design of the RAG pipeline, ensuring the integration of best-in-class models and technologies.
    • Guided the team in selecting appropriate tools and frameworks, such as AWS SageMaker for model training and deployment, and Amazon Elasticsearch Service for scalable vector search.
    • Oversaw the implementation of the cross-modal attention mechanism and the integration of personalization layers using Amazon Personalize, ensuring technical robustness and scalability.
  4. Resource Management and Allocation:
    • Managed project resources, including budget allocation, personnel assignments, and infrastructure provisioning.
    • Ensured efficient utilization of AWS resources, optimizing costs through strategies like spot instances and auto-scaling configurations.
  5. Stakeholder Communication and Reporting:
    • Acted as the primary liaison between the project team and senior leadership, providing regular updates on progress, challenges, and key achievements.
    • Developed detailed technical documentation and reports to communicate complex concepts and project outcomes to non-technical stakeholders.
  6. Quality Assurance and Risk Management:
    • Implemented rigorous testing protocols, including unit tests, integration tests, and performance benchmarks, to ensure the pipeline met quality standards.
    • Identified potential risks, such as data privacy concerns and model performance issues, and developed mitigation strategies to address them proactively.
  7. Continuous Improvement and Iteration:
    • Established feedback loops with end-users to gather insights and identify areas for improvement.
    • Led iterative development cycles, incorporating user feedback and performance data to refine and enhance the RAG pipeline continuously.

Result: Under my leadership, the project successfully delivered a robust multimodal RAG pipeline that significantly enhanced our AI services. The pipeline achieved a 30% reduction in response time and a 25% increase in customer satisfaction scores. The project was completed on time and within budget, and the scalable architecture facilitated its adoption across multiple AWS departments, driving further innovation and improvements in AI-driven customer support.

Reflection: This role reinforced the importance of balancing technical expertise with strategic leadership. I learned that effective project management requires not only a deep understanding of the technical aspects but also the ability to inspire and coordinate a diverse team towards a common goal. Additionally, proactive stakeholder engagement and transparent communication are crucial for aligning expectations and ensuring project success. This experience has honed my ability to lead complex, high-impact projects and reinforced my commitment to fostering a culture of innovation and excellence.

5. Describe a time when you had to manage competing priorities.

Situation: While managing the Generative AI team at AWS, two high-priority projects emerged simultaneously: the development of a Multimodal RAG pipeline and the enhancement of our existing NLP-based query understanding system for Alexa. Both projects were critical for improving customer interactions and had overlapping resource requirements, creating a conflict in prioritization.

Task: I needed to effectively manage and balance these competing priorities to ensure that both projects progressed without compromising on quality or deadlines. This required strategic resource allocation, clear communication, and the ability to adapt plans based on evolving project needs.

Action:

  1. Assessment and Prioritization:
    • Conducted a comprehensive analysis of both projects’ strategic importance, potential impact, resource requirements, and timelines.
    • Engaged with key stakeholders from both projects to understand their objectives, dependencies, and critical milestones.
    • Utilized a weighted scoring model to evaluate each project based on factors such as business impact, customer value, and alignment with organizational goals.
  2. Resource Allocation:
    • Identified overlapping resource needs and assessed the team’s capacity and expertise.
    • Reallocated resources by temporarily shifting some team members from the query understanding project to support the initial phases of the RAG pipeline, ensuring that critical tasks for both projects were adequately staffed.
    • Secured additional temporary resources by collaborating with HR to onboard contract developers with specialized skills in multimodal AI.
  3. Timeline Adjustment and Milestone Management:
    • Revised project timelines to incorporate the adjusted resource allocation, setting realistic deadlines that accounted for the concurrent demands.
    • Established clear, incremental milestones for both projects to monitor progress and ensure that neither project fell behind schedule.
    • Implemented buffer periods to accommodate unforeseen challenges and maintain flexibility in the project timelines.
  4. Enhanced Communication and Coordination:
    • Scheduled bi-weekly cross-project status meetings to provide updates, identify bottlenecks, and facilitate information sharing between the two teams.
    • Created a shared dashboard using AWS QuickSight to visualize project progress, resource utilization, and key performance indicators, ensuring transparency and informed decision-making.
  5. Risk Management:
    • Identified potential risks related to resource contention, such as burnout and decreased productivity, and implemented mitigation strategies including workload balancing and regular check-ins.
    • Developed contingency plans to address possible delays or resource shortages, ensuring that critical project components remained on track.
  6. Delegation and Empowerment:
    • Empowered team leads to make tactical decisions regarding task assignments and workflow optimizations, fostering a sense of ownership and accountability.
    • Provided the necessary support and autonomy for team members to manage their responsibilities effectively within the new prioritization framework.

Result: Both projects were successfully delivered on time and met their respective objectives. The Multimodal RAG pipeline was launched, resulting in a 30% improvement in response accuracy and a 25% increase in customer satisfaction. Simultaneously, the enhanced query understanding system for Alexa achieved a 20% reduction in misinterpretation rates and a 15% boost in personalized response effectiveness. Efficient management of competing priorities ensured that neither project experienced significant delays or resource shortages, maintaining overall team morale and productivity.

Reflection: Managing competing priorities taught me the critical importance of strategic planning and flexible resource management. I learned that thorough assessment and clear prioritization frameworks are essential for making informed decisions when faced with multiple high-stakes projects. Additionally, maintaining open lines of communication and fostering a collaborative environment can mitigate the challenges of resource contention. This experience reinforced my ability to navigate complex project landscapes, ensuring that organizational goals are met without compromising on quality or team well-being.

6. How do you maintain the team’s project priorities and help resolve priority conflicts?

Situation: In my role leading the Generative AI organization at AWS, I often oversee multiple high-impact projects simultaneously. Maintaining clear project priorities and resolving conflicts is crucial to ensuring that our team remains focused and productive. For instance, during the development of our Multimodal RAG pipeline, we faced priority conflicts with an ongoing initiative to enhance our NLP-based query understanding system.

Task: My task was to establish and maintain a structured approach to prioritize projects effectively, ensuring alignment with organizational goals while addressing and resolving any priority conflicts that arose between different initiatives.

Action:

  1. Establishing Clear Prioritization Criteria:
    • Defined a set of prioritization criteria based on strategic alignment, business impact, customer value, resource requirements, and urgency.
    • Collaborated with senior leadership to ensure that these criteria reflected the broader organizational objectives and received their endorsement.
  2. Implementing a Structured Prioritization Framework:
    • Adopted the RICE (Reach, Impact, Confidence, Effort) scoring model to evaluate and rank projects systematically.
    • Conducted regular prioritization meetings with key stakeholders to assess and update project priorities based on the latest data and business needs.
  3. Transparent Communication and Documentation:
    • Created a centralized project management dashboard using AWS QuickSight, providing real-time visibility into project statuses, priorities, and resource allocations.
    • Maintained detailed documentation outlining the rationale behind prioritization decisions, ensuring transparency and accountability.
  4. Regular Prioritization Reviews:
    • Held monthly review sessions to reassess project priorities in light of new developments, changing market conditions, or shifts in strategic focus.
    • Encouraged open dialogue during these sessions, allowing team members and stakeholders to voice concerns, provide insights, and suggest adjustments.
  5. Resource Allocation and Flexibility:
    • Utilized resource planning tools to map out team members’ capacities and allocate resources dynamically based on current priorities.
    • Implemented a flexible resource pool, allowing for quick reallocation of personnel to high-priority projects as needed without disrupting ongoing work.
  6. Conflict Resolution Mechanisms:
    • Established a clear escalation path for resolving priority conflicts, involving senior leadership when necessary to make final decisions.
    • Mediated discussions between project leads to find mutually agreeable solutions, such as adjusting timelines, redistributing tasks, or scaling project scopes.
  7. Empowering Team Leads:
    • Empowered team leads to make tactical decisions regarding task prioritization within their teams, fostering a sense of ownership and accountability.
    • Provided training on effective prioritization techniques and conflict resolution strategies to enhance their ability to manage their teams effectively.
  8. Monitoring and Feedback Loops:
    • Continuously monitored project progress and resource utilization through automated reporting and analytics.
    • Implemented feedback loops where team members could provide input on prioritization effectiveness, allowing for continuous improvement of the prioritization process.

Result: By implementing a structured prioritization framework and fostering transparent communication, the team was able to maintain clear project priorities and effectively manage competing demands. This approach led to a 20% increase in project delivery efficiency and a 15% improvement in team satisfaction scores, as team members felt more empowered and less conflicted about their workloads. Additionally, the ability to swiftly resolve priority conflicts minimized disruptions and ensured that critical projects received the necessary attention and resources.

Reflection: Maintaining team project priorities and resolving conflicts requires a balance of structured frameworks and flexible, transparent communication. I learned that establishing clear criteria and processes for prioritization provides a solid foundation for decision-making, while fostering an open and collaborative environment enables effective conflict resolution. Empowering team leads and maintaining visibility into project statuses further enhances the team’s ability to stay aligned and responsive to changing needs. This experience has reinforced the importance of strategic planning and adaptive leadership in managing complex, multi-project environments.

Conflict Management:

  • How did you handle conflict or disagreement with a colleague?
  • Describe a time when you had to make a decision that conflicted with others.
  • Any time you disagreed with your manager?
  • How do you convince others or handle conflicts?
  • How do you handle engineers in the team who only want to work on high-visibility projects?
  • How do you resolve disputes with other teams?
  • How do you keep the project running when faced with a pushy project manager (PM)?

1. How did you handle conflict or disagreement with a colleague?

Situation: While leading the Generative AI team at AWS, I encountered a disagreement with a senior engineer on the approach to integrating a cross-modal attention mechanism into our multimodal RAG pipeline. The engineer believed that we should use a pre-existing off-the-shelf implementation, whereas I advocated for a custom-designed mechanism tailored to our specific use case, considering the performance requirements and data constraints.

Task: I needed to address the disagreement constructively, ensuring that we arrived at a technically sound decision that would meet the project’s performance and scalability goals, while also maintaining team morale and avoiding friction.

Action:

  1. Listen to Understand:
    • I scheduled a one-on-one meeting with the engineer to understand his rationale for preferring the off-the-shelf solution. I listened carefully to his concerns about the complexity and potential risks of building a custom solution from scratch, such as increased development time and debugging challenges.
  2. Articulate My Position:
    • I then presented my reasoning for advocating a custom approach. I highlighted the specific challenges we faced, such as the need for highly optimized performance due to the scale of data we were handling. I pointed out that the off-the-shelf solution might not adequately meet our latency and cross-modal alignment requirements.
  3. Back to Data and Prototyping:
    • To move forward, I proposed a compromise: we would run a quick benchmarking exercise where we compared both approaches using a representative subset of our data. We would evaluate both solutions based on performance metrics such as inference speed, accuracy, and memory usage, as well as the ease of integration.
  4. Benchmarking and Analysis:
    • We collaboratively implemented both approaches in a limited test environment. Over a period of two weeks, we analyzed the results, finding that the custom approach provided a 20% improvement in cross-modal retrieval accuracy and a 15% reduction in latency, albeit with a slightly longer development time.
  5. Decision Making:
    • With the data in hand, the engineer agreed that the custom solution provided long-term benefits that outweighed the short-term convenience of the off-the-shelf solution. We decided to move forward with the custom implementation, allocating additional resources to support the increased development effort.

Result: The conflict was resolved amicably, and the custom attention mechanism ultimately contributed to a 25% increase in the overall performance of the multimodal RAG pipeline. The engineer appreciated the data-driven approach and felt that his concerns were heard and addressed, which strengthened our working relationship and the trust within the team.

Reflection: This experience reinforced the importance of open communication and data-driven decision-making when resolving conflicts. By fostering a collaborative environment where all perspectives are considered and by backing decisions with empirical evidence, I was able to turn a disagreement into an opportunity for innovation and stronger team cohesion.

2. Describe a time when you had to make a decision that conflicted with others.

Situation: During a critical phase of the multimodal RAG pipeline project at AWS, we faced a decision on whether to prioritize scaling the pipeline’s infrastructure or improving model accuracy. The data science team was eager to invest more time fine-tuning the model for incremental gains in accuracy, but our operations and infrastructure team raised concerns about the need for scaling to handle the expected increase in data volume and query traffic.

Task: As the project lead, I needed to make a decision that would balance both perspectives while ensuring that the project stayed on track and met our performance goals. This involved potentially conflicting priorities between improving accuracy and ensuring the system could handle scale.

Action:

  1. Gather Data and Perspectives:
    • I organized a cross-functional meeting with representatives from both the data science and operations teams. I asked each team to present their case, focusing on the projected impact of their respective priorities on system performance, user experience, and long-term scalability.
  2. Cost-Benefit Analysis:
    • I performed a cost-benefit analysis of both approaches. From a technical standpoint, further model tuning could lead to a 2-3% accuracy improvement, while scaling infrastructure would ensure we could handle a 3x increase in traffic without downtime. I also factored in customer impact, business objectives, and resource constraints.
  3. Prioritizing Scalability:
    • After considering both options, I decided to prioritize scaling the infrastructure. The reasoning was that while model accuracy gains were valuable, ensuring a scalable system was crucial to meeting business demand and avoiding potential outages or latency issues. I communicated this decision to both teams, emphasizing the importance of stability and customer experience at this stage of the project.
  4. Plan for Model Accuracy:
    • To address the concerns of the data science team, I committed to revisiting model fine-tuning in the next phase of the project, once the infrastructure scaling was complete. I set up a clear timeline and allocated resources to ensure that their efforts would be prioritized after we had achieved the necessary scalability.

Result: The decision to prioritize scaling allowed us to successfully handle the projected increase in traffic without any service interruptions or degradation in performance. Once the infrastructure was stabilized, we returned to fine-tuning the model, ultimately achieving both scalability and accuracy improvements. The project was delivered on time and met both operational and customer satisfaction metrics.

Reflection: This experience taught me the importance of making difficult decisions that may not please everyone but are in the best interest of the project and the business. I learned that clear communication, transparency, and the ability to articulate the reasoning behind a decision are critical in gaining buy-in from all stakeholders, even when they initially disagree.

3. Any time you disagreed with your manager?

Situation: During my time as a manager in Alexa AI, we were working on a large-scale query understanding project that involved refining the NLP model used for interpreting user queries. My manager wanted to prioritize optimizing the model for rare query types, which accounted for less than 5% of total traffic, while I believed we should focus on improving performance for more common query types that had a higher overall impact on user experience.

Task: I needed to present my perspective in a way that respected my manager’s position but also demonstrated the greater business impact of focusing on more frequent query types.

Action:

  1. Data-Driven Argument:
    • I conducted a detailed analysis of our query logs, focusing on the distribution of query types and their respective impacts on user satisfaction. My analysis showed that optimizing for common query types would improve performance for 80% of interactions, leading to a significant improvement in overall user experience.
  2. Preparing for Discussion:
    • I prepared a presentation that highlighted the key data points, including the projected impact on KPIs such as customer satisfaction and system performance. I also outlined the potential trade-offs of focusing on rare query types, such as the limited impact on overall metrics despite the higher complexity.
  3. Respectful Dialogue:
    • I scheduled a one-on-one meeting with my manager to discuss the issue. I presented my analysis and proposed that we prioritize the high-frequency queries in the next iteration, while dedicating a smaller team to explore optimizations for the rare queries in parallel. This way, both objectives could be addressed without compromising the overall project timeline.
  4. Compromise and Alignment:
    • My manager appreciated the data-driven approach and agreed to shift the focus toward optimizing for the more frequent queries, with a plan to revisit rare query optimizations in a subsequent phase. This compromise allowed us to maintain alignment on project goals while ensuring that the most impactful improvements were made first.

Result: The focus on high-frequency queries led to a 15% increase in overall user satisfaction and a 10% reduction in query misinterpretation rates. Later, we were able to circle back to the rare query types, achieving further incremental improvements. My manager acknowledged the success of the approach, and our working relationship remained strong, built on mutual respect and open communication.

Reflection: This experience reinforced the importance of backing up disagreements with data and presenting alternative solutions. I learned that respectful and thoughtful communication can turn disagreements into productive discussions, leading to better outcomes for the project and the team. It also highlighted the value of compromise and maintaining a focus on the bigger picture.

4. How do you convince others or handle conflicts?

Situation: While leading a cross-team project at AWS to integrate personalization features into a generative AI system, there was a disagreement between the data engineering team and the machine learning team regarding the priority of certain tasks. The data engineering team wanted to prioritize data pipeline optimizations, while the machine learning team pushed for earlier model integration to start fine-tuning the personalization algorithms.

Task: My role was to mediate the conflict and convince both teams to align on a unified plan that balanced the needs of data pipeline optimization with the model integration work, ensuring that neither team’s objectives were neglected.

Action:

  1. Facilitating Open Discussion:
    • I organized a joint meeting with representatives from both teams and created a structured agenda to ensure that everyone had an opportunity to voice their concerns. I encouraged a collaborative atmosphere where the teams could openly discuss their priorities, dependencies, and constraints.
  2. Focusing on Common Goals:
    • I redirected the conversation to the common goals of the project: delivering a scalable, high-performing personalization feature. I reminded both teams that the success of the project depended on both robust data infrastructure and high-quality models, and neither could be compromised.
  3. Data-Driven Decision Making:
    • I proposed a data-driven approach to resolve the conflict. We analyzed the current state of the data pipeline and model integration to identify potential bottlenecks and prioritize tasks that would unlock value for both teams. For example, we identified a critical dependency in the data pipeline that needed to be resolved before effective model training could begin.

This created a natural prioritization of tasks that both teams could agree on.

  1. Building Consensus:
    • I facilitated a consensus by proposing a phased approach: the data engineering team would focus on resolving key pipeline bottlenecks in the first two weeks, after which the machine learning team would begin their model integration work with the updated pipeline. This staggered approach ensured both teams could work efficiently without stepping on each other’s toes.
  2. Monitoring and Communication:
    • I set up regular check-ins to ensure the phased approach was working as intended and that both teams felt their concerns were being addressed. These meetings also provided an opportunity to adjust the plan if needed, ensuring ongoing alignment.

Result: The phased approach led to the timely completion of both the data pipeline optimizations and the model integration work, resulting in a successful launch of the personalized AI system. The system achieved a 20% improvement in user engagement metrics post-launch. Both teams appreciated the collaborative resolution process and were able to work together more effectively moving forward.

Reflection: This experience taught me the value of facilitating open communication and focusing on shared objectives when handling conflicts. By creating an environment where all voices were heard and focusing on data-driven decisions, I was able to guide the teams toward a resolution that satisfied both parties. I learned that conflicts can often be opportunities for growth when managed effectively, leading to stronger collaboration and better project outcomes.

5. How do you handle engineers in the team who only want to work on high-visibility projects?

Situation: While managing a team at AWS, I noticed that some of the engineers were gravitating toward high-visibility projects, such as developing front-end features for generative AI products, and were less interested in the backend or infrastructure tasks that were equally critical to the success of the project.

Task: I needed to ensure that all aspects of the project were adequately staffed and that the less glamorous but equally important tasks were completed without demotivating the engineers who preferred high-visibility work.

Action:

  1. Understanding Motivations:
    • I had one-on-one conversations with the engineers to understand their motivations for wanting to work on high-visibility projects. For many, it was about career growth and being able to showcase their work for promotions or recognition.
  2. Clarifying the Importance of All Work:
    • I emphasized the importance of infrastructure and backend work by clearly articulating how these tasks directly contributed to the overall success of the high-visibility features. I showed them the bigger picture, such as how robust data pipelines and scalable infrastructure enabled the front-end features to function smoothly.
  3. Balanced Project Assignments:
    • I implemented a more balanced project assignment strategy. I assigned engineers a mix of high-visibility and backend/infrastructure tasks, ensuring that everyone had opportunities to work on visible, impactful projects while still contributing to critical backend work.
  4. Highlighting Contributions:
    • I made a point to highlight the contributions of engineers working on less-visible tasks in team meetings and project reports. This helped raise the visibility of their work within the organization and ensured that their efforts were recognized by leadership.
  5. Career Development Opportunities:
    • I worked with engineers to identify how backend and infrastructure work could contribute to their career goals. For example, I encouraged engineers to become subject matter experts in areas like system scalability or data processing, positioning them for future leadership roles in those areas.
  6. Mentorship and Rotation:
    • I introduced mentorship and rotation programs, where engineers working on high-visibility features could mentor those on infrastructure tasks, and vice versa. This created opportunities for skill-building and fostered a culture of cross-functional collaboration.

Result: The team became more balanced, with engineers taking ownership of both high-visibility and critical backend tasks. The rotation program helped engineers develop a broader skill set, which improved overall team versatility. As a result, project deadlines were met more consistently, and engineers reported higher satisfaction with their work as they felt their contributions were valued across the board.

Reflection: This experience reinforced the importance of clear communication and recognition in balancing project work across a team. By ensuring that all contributions are valued and aligning tasks with individual career goals, it’s possible to motivate engineers to engage in a variety of work while still ensuring that critical tasks are completed. I learned that fostering a culture of shared ownership and cross-functional development can enhance both individual growth and team performance.

6. How do you resolve disputes with other teams?

Situation: During the development of an AI-powered recommendation system at AWS, there was a dispute between our team and the data privacy team over the handling of user data. The privacy team had concerns about the way we were storing and processing user data for personalization, which they believed could expose us to compliance risks.

Task: I needed to resolve the dispute in a way that ensured our recommendation system could continue to provide personalized experiences while adhering to the strict privacy and compliance standards set by the organization.

Action:

  1. Understanding Concerns:
    • I set up a meeting with the privacy team to fully understand their concerns. They outlined specific issues related to data retention policies, user consent, and potential exposure to GDPR violations if data wasn’t handled correctly.
  2. Collaboration and Compromise:
    • I worked with the privacy team to identify alternative methods of achieving the same personalization goals without compromising user privacy. We explored options such as anonymizing user data, implementing stricter encryption methods, and reducing data retention periods while still allowing for effective personalization.
  3. Technical Adjustments:
    • I directed the engineering team to implement the agreed-upon changes, such as integrating encryption protocols and updating data storage practices to meet compliance requirements. We also adjusted our machine learning models to work with anonymized data where possible, minimizing the amount of personally identifiable information (PII) processed by the system.
  4. Cross-Functional Workshops:
    • I organized cross-functional workshops with engineers and privacy experts to ensure that everyone had a clear understanding of the new privacy protocols and how they impacted the system’s design. This helped bridge the gap between technical goals and compliance needs.
  5. Documentation and Monitoring:
    • We developed comprehensive documentation detailing the new data handling procedures and implemented monitoring systems to ensure ongoing compliance with privacy regulations. These systems included regular audits and automated alerts for any potential data breaches or policy violations.

Result: The dispute was resolved, and we successfully launched the recommendation system with enhanced privacy protections. The system maintained a high level of personalization while ensuring full compliance with data privacy regulations, reducing our risk of legal exposure. Additionally, the collaboration with the privacy team strengthened inter-departmental relationships and set a precedent for future projects.

Reflection: This experience taught me the importance of collaboration and flexibility when resolving disputes between teams with different priorities. By actively engaging with the privacy team and seeking a solution that addressed their concerns without sacrificing project goals, we were able to find a compromise that worked for both sides. I learned that technical and compliance teams can work together to achieve mutually beneficial outcomes when there is open communication and a willingness to adjust approaches.

7. How do you keep the project running when faced with a pushy project manager (PM)?

Situation: While leading a machine learning project at AWS, I worked with a project manager who was highly focused on aggressive timelines and constantly pushed the team to deliver faster, sometimes at the expense of thorough testing and quality assurance. This created tension within the team, as engineers were concerned about the potential impact on system stability and long-term performance.

Task: I needed to manage the PM’s expectations while ensuring that the project maintained high standards of quality and met critical milestones without burning out the team or compromising the integrity of the work.

Action:

  1. Establishing Clear Priorities:
    • I had a candid conversation with the project manager to better understand their urgency and the pressures they were facing from upper management. I then outlined the risks associated with rushing development, such as increased technical debt and potential failures in production, which could ultimately delay the project further if not addressed.
  2. Compromise and Realistic Milestones:
    • We agreed to set more realistic milestones by breaking the project down into smaller, incremental deliverables. This allowed the PM to see progress being made while giving the engineering team enough time to properly test and validate each phase of the project.
  3. Buffer Time for Testing:
    • I advocated for the inclusion of buffer time in the project schedule specifically for testing, debugging, and quality assurance. I emphasized the importance of these activities in preventing costly rollbacks or issues post-launch, providing examples from past projects where inadequate testing had led to delays.
  4. Regular Check-ins and Transparency:
    • I instituted regular check-ins with the project manager, providing transparent updates on progress and flagging any potential risks early. This helped manage their expectations and provided a forum to adjust timelines if needed based on the actual progress of the work.
  5. Engaging Leadership Support:
    • I engaged with senior leadership to communicate the importance of balancing speed with quality. By aligning leadership with the long-term vision of the project and gaining their support, I was able to alleviate some of the pressure coming from the project manager, who became more receptive to the idea of balancing timelines with quality control.

Result: The project was completed on time with no major incidents or quality issues. The staggered milestones allowed the team to maintain a steady pace without sacrificing testing and validation. As a result, the system performed reliably post-launch, and the PM acknowledged that the extra time for quality assurance had been beneficial in preventing delays or costly fixes down the road.

Reflection: This experience taught me the importance of managing expectations and balancing the demands for speed with the need for quality. By establishing clear communication, advocating for realistic timelines, and engaging leadership support, I was able to navigate the pressure from a pushy project manager without compromising

the integrity of the project. It reinforced the value of transparency, incremental progress, and maintaining a focus on long-term success over short-term gains.

Feedback & Growth:

  • Tell me about a time when you received negative feedback from your manager or peers.
  • How did you deal with negative feedback, especially feedback you resisted taking?
  • Tell me about a time when you accepted feedback from your manager.
  • Describe a time when you provided constructive feedback.
  • What aspects do you want to improve the most?
  • What new skills do you hope to develop?

1. Tell me about a time when you received negative feedback from your manager or peers.

Situation: During a large-scale project to improve our AI-driven recommendation system at AWS, I received feedback from my manager regarding my approach to stakeholder communication. Specifically, my manager felt that I wasn’t providing enough detailed updates to non-technical stakeholders, which led to confusion about project progress and goals. I had been focusing heavily on the technical aspects of the project and assumed that the stakeholders were more familiar with the technical language and jargon than they actually were.

Task: I needed to address this feedback by improving the clarity and frequency of my communications with non-technical stakeholders, ensuring they felt informed and aligned with the project’s progress without overwhelming them with unnecessary technical detail.

Action:

  1. Acknowledge and Reflect:
    • I took the feedback seriously and reflected on how my communication style may have been causing misunderstandings. I recognized that while I was providing in-depth technical updates, I wasn’t always tailoring my message to the audience, particularly the non-technical stakeholders.
  2. Seek Clarification:
    • I had a follow-up conversation with my manager to fully understand their expectations regarding communication. They emphasized the need for concise, clear updates that focused more on business impact, progress toward milestones, and potential risks.
  3. Action Plan:
    • I adjusted my approach by developing a new communication framework for project updates. I started breaking down technical information into high-level summaries and focused more on explaining the “why” behind the technical decisions rather than the “how.”
    • I introduced visual aids, such as dashboards and progress charts, using tools like AWS QuickSight to give non-technical stakeholders a clearer understanding of the project’s status and impact without delving into the technical details.
  4. Increased Communication Frequency:
    • I increased the frequency of updates, sending out weekly summaries with bullet points that highlighted key achievements, upcoming milestones, and any risks that needed attention. I made sure to tie everything back to business outcomes, ensuring that the stakeholders could easily grasp the relevance of the updates to the overall project goals.

Result: The improved communication approach resulted in a much clearer understanding of the project among non-technical stakeholders. The feedback loop became more positive, and the stakeholders felt more engaged and informed. My manager acknowledged the improvement, and it also contributed to smoother decision-making during the project’s execution as stakeholders were more aligned with the project’s progress and objectives.

Reflection: This experience taught me that effective communication is not one-size-fits-all; it must be tailored to the audience’s needs and level of understanding. I learned to adapt my communication style to ensure that technical and non-technical stakeholders alike felt included and informed. This feedback helped me develop a more strategic approach to communication, which has since been crucial in managing cross-functional teams and large projects.

2. How did you deal with negative feedback, especially feedback you resisted taking?

Situation: In my early days as a manager at Alexa AI, I received feedback from a senior engineer who felt that I was micromanaging certain aspects of the project. They believed that I was too involved in the day-to-day technical decisions, which was demotivating for the engineering team and created bottlenecks in the workflow. Initially, I resisted this feedback because I believed that my involvement was necessary to ensure the quality and direction of the project.

Task: I needed to address the feedback and reassess my management style, particularly in terms of delegation and empowering the team to take ownership of their tasks, while still ensuring that the project met its quality and timeline goals.

Action:

  1. Self-Reflection and Realization:
    • Although I resisted the feedback at first, I took some time to reflect on it. I realized that my desire for control was rooted in my technical background, and I had to shift from being a technical contributor to being a leader who trusted the team’s expertise.
  2. Seek Additional Perspectives:
    • I reached out to a few other team members for their honest opinions. They confirmed that while they appreciated my technical knowledge, they felt that they weren’t being given enough autonomy to make decisions on their own.
  3. Action Plan for Delegation:
    • I developed an action plan to gradually step back from the day-to-day technical decisions and instead focus more on strategic guidance and removing obstacles for the team. I started by delegating more decision-making authority to the senior engineers, allowing them to take ownership of key components of the project.
  4. Clear Communication and Trust-Building:
    • I communicated clearly to the team that I trusted their expertise and that my role moving forward would be to support them rather than to dictate solutions. I made it a point to ask questions rather than give directives, fostering an environment where the team could freely discuss ideas and solutions.
  5. Monitor Progress Without Micromanaging:
    • I implemented a regular check-in schedule where I would stay informed about progress and challenges without delving into the granular details unless necessary. This allowed me to stay involved at a higher level while giving the team the freedom to execute their tasks independently.

Result: As a result, the team became more engaged and motivated. They appreciated the trust I placed in them, and productivity increased as engineers felt empowered to make decisions. The project continued to meet its goals, and the team developed stronger problem-solving capabilities. I also received positive feedback from the engineer who had initially raised the concern, which validated the changes I had made.

Reflection: This experience taught me that transitioning from a technical role to a leadership role requires a shift in mindset from “doing” to “enabling.” While it can be difficult to let go of control, especially for someone with a strong technical background, I learned that empowering the team leads to better outcomes and fosters a more positive and collaborative environment. Trusting the team and providing strategic guidance rather than micromanaging has become a core aspect of my management style.

3. Tell me about a time when you accepted feedback from your manager.

Situation: During my time at AWS, my manager provided feedback on my approach to sprint planning and resource allocation. They observed that I was often too focused on short-term goals and meeting immediate deadlines, which sometimes led to resource allocation imbalances and insufficient attention to long-term planning, such as technical debt management and capacity building.

Task: I needed to address this feedback by adjusting my approach to sprint planning and resource allocation, ensuring that I balanced short-term delivery with long-term sustainability and team growth.

Action:

  1. Acknowledge the Feedback:
    • I acknowledged the feedback from my manager and realized that while I was good at driving short-term execution, I hadn’t been allocating enough time or resources to addressing technical debt or building capabilities that would benefit future projects.
  2. Revisiting the Sprint Planning Process:
    • I revamped the sprint planning process to ensure that each sprint included a mix of immediate deliverables and tasks focused on long-term improvements. This included dedicating 10-15% of each sprint to addressing technical debt, infrastructure improvements, and skill development for team members.
  3. Prioritization Framework:
    • I introduced a prioritization framework that balanced urgent tasks with long-term priorities. We started categorizing tasks into “must-have” for short-term goals and “should-have” for long-term sustainability. This helped ensure that both immediate deliverables and long-term objectives were consistently represented in our planning.
  4. Regular Strategic Reviews:
    • I implemented quarterly strategic reviews with the team where we would take a step back from day-to-day operations to discuss broader goals, technical debt, and areas where we could invest in future-proofing our systems. These sessions allowed us to proactively identify issues that could become roadblocks down the line.
  5. Monitoring and Adjustment:
    • I set up a system to monitor progress toward both short-term and long-term goals, using project management tools like Jira to ensure that the balance between immediate needs and future needs was maintained. I also ensured that we adjusted the balance when necessary based on evolving project demands.

Result: The changes led to a more balanced approach to sprint planning, where we consistently met short-term deadlines while also reducing technical debt and building more robust systems for the future. Over time, this approach led to fewer fire drills, improved system performance, and increased team capacity for handling more complex projects. My manager noted the improvement in long-term planning and resource management, and the team appreciated the focus on sustainability.

Reflection: This experience taught me the importance of balancing immediate execution with long-term planning. Accepting the feedback from my manager helped me become more strategic in my approach to project management, ensuring that we weren’t just meeting short-term goals but also laying the groundwork for future success. It reinforced the need to always consider both the present and the future when leading a team, and it helped me develop a more holistic view of resource management.

4. Describe a time when you provided constructive feedback.

Situation: As a manager at AWS, I had a junior engineer on my team who was technically proficient but struggled with time management. They often took on too many tasks at once and missed deadlines, which affected the team’s overall productivity. This was starting to cause friction within the team, and I needed to provide constructive feedback to help them improve their workflow.

Task: My task was to provide constructive feedback to the engineer in a way that would help them improve their time management skills without discouraging them or undermining their confidence.

Action:

  1. Private and Timely Feedback:
    • I scheduled a one-on-one meeting with the engineer in a private setting to ensure they felt comfortable and respected. I made sure to provide the feedback soon after the issue became apparent, so it was still fresh and relevant.
  2. Starting with Positives:
    • I began the

conversation by acknowledging their technical strengths and their willingness to take on challenging tasks. I emphasized that their ambition was valued, but that it was important to balance ambition with effective time management to ensure quality and delivery.

  1. Highlighting the Impact:
    • I provided specific examples of how their time management issues were affecting the team, such as delayed deliverables and increased pressure on other team members. I framed this not as a criticism, but as an opportunity for improvement that would benefit both them and the team.
  2. Actionable Suggestions:
    • I offered concrete, actionable suggestions for improvement, such as breaking tasks into smaller, more manageable chunks, setting realistic timelines, and using project management tools like Trello to track progress and avoid overcommitting. I also recommended regular check-ins with their peers or with me to ensure they were staying on track.
  3. Follow-Up and Support:
    • I followed up with the engineer a few weeks later to check on their progress. I provided additional support and coaching, helping them prioritize tasks and refine their workflow. I also ensured that they had access to time management resources, including mentorship from more experienced team members.

Result: The engineer responded positively to the feedback and gradually improved their time management skills. Within a few months, their ability to prioritize tasks and meet deadlines improved significantly, and their performance became more consistent. This, in turn, reduced friction within the team and contributed to a smoother, more efficient workflow.

Reflection: This experience reinforced the importance of providing constructive feedback in a way that is supportive, specific, and actionable. I learned that addressing performance issues early and offering practical solutions can help team members grow without feeling discouraged. The positive outcome also highlighted the value of follow-up and ongoing support to ensure lasting improvement.

5. What aspects do you want to improve the most?

Personal Time Management and Delegation:

  • As a manager, I want to continue improving my ability to balance hands-on technical work with strategic leadership. I sometimes find myself getting too involved in the technical details, which can limit my time for higher-level decision-making and team development. Improving my delegation skills and focusing more on empowering my team to take ownership of tasks will help me strike a better balance between technical involvement and leadership.

Cross-Functional Communication:

  • While I’ve made significant progress in adapting my communication style for non-technical stakeholders, I believe there is still room for improvement. I want to refine my ability to simplify complex concepts and ensure that communication with stakeholders across all levels of the organization remains clear and effective. This will involve continuous learning, practicing public speaking, and gathering feedback on how well my messages resonate with diverse audiences.

Long-Term Strategic Planning:

  • I want to improve my ability to incorporate long-term strategic planning into day-to-day project management. This includes anticipating future challenges, identifying opportunities for innovation, and ensuring that my team is not only meeting immediate goals but also building capabilities that will support the organization’s growth over the next several years.

6. What new skills do you hope to develop?

Advanced Multimodal AI Techniques:

  • While I have experience in building multimodal RAG pipelines, I want to deepen my knowledge of cutting-edge multimodal AI techniques, particularly in areas such as video understanding and cross-modal transfer learning. These areas are rapidly evolving, and mastering them will help me lead my team in developing more sophisticated and capable AI systems that can handle a wider range of inputs and contexts.

People Management and Coaching:

  • As I continue to grow in my leadership role, I want to further develop my skills in people management and coaching. Specifically, I aim to enhance my ability to mentor and develop the next generation of leaders within my team. This includes learning more about coaching techniques, emotional intelligence, and how to inspire and motivate diverse teams to achieve their full potential.

Business and Product Strategy:

  • I want to strengthen my understanding of business and product strategy, particularly in the context of AI-driven solutions. Developing a deeper knowledge of how AI products fit into broader business goals will enable me to make more informed decisions and contribute more effectively to the overall strategy of the organization. This may involve pursuing additional coursework or certifications in product management and business strategy.

Public Speaking and Advocacy:

  • Public speaking is a skill I want to hone further, especially as it relates to advocating for AI initiatives and presenting complex technical concepts to executive leadership or external stakeholders. This involves not only becoming more confident in my delivery but also learning how to tailor my presentations to resonate with different audiences, including non-technical decision-makers.

Decision-Making:

  • Describe a time when you had to make a decision with insufficient information.
  • Describe a time when you had to make a quick decision.
  • Describe a time when you insisted on doing something, but it turned out to be wrong.
  • What is the mistake you’ve made or a decision in work you regret?
  • Biggest failure or most regrettable project?
  • Tell me about a time when you were solving a problem that was ambiguous.

1. Describe a time when you had to make a decision with insufficient information.

Situation: While leading the Generative AI team at AWS, we were in the early stages of designing a new recommendation system using multimodal data, including text, images, and voice. We needed to decide on the architecture for the recommendation engine but had limited data on the specific patterns of how users would interact with this new feature, as it was yet to be fully deployed in production.

Task: I had to make a decision on whether to implement a hybrid recommendation system that combined collaborative filtering with content-based filtering or to rely solely on content-based recommendations. This decision needed to be made before we had a clear understanding of the user interaction patterns for the new multimodal inputs, which added significant uncertainty.

Action:

  1. Gather What Was Available:
    • I collected whatever data we had from past projects that involved similar systems, focusing on user behavior with text and image data, though it wasn’t directly applicable to multimodal scenarios.
    • I reached out to other teams within AWS that had implemented multimodal AI solutions to gather their insights and challenges, though their use cases differed from ours.
  2. Mitigate Uncertainty with a Flexible Approach:
    • Recognizing the limited data, I decided to adopt a flexible, modular architecture. We implemented a hybrid recommendation engine that could support both collaborative filtering and content-based filtering but built it in a way that allowed us to disable or prioritize one method over the other based on user behavior data once it became available.
    • We developed the architecture with feature flags, allowing us to toggle between different recommendation strategies without redeploying the entire system. This way, we could make adjustments as more data became available.
  3. Monitor and Iterate:
    • I set up a comprehensive monitoring system to track user interactions once the system went live, with detailed logging of how users engaged with recommendations from different modalities (text, image, voice).
    • I also put in place a mechanism for rapid iteration, where we could quickly adjust the recommendation strategy based on real-time data from user interactions.

Result: The flexible architecture allowed us to make informed adjustments once we had more user data. Initially, content-based filtering proved more effective due to the limited number of users in the early stages, but as more user data accumulated, we successfully transitioned to a hybrid model, improving recommendation relevance by 15%. The decision to build a modular system mitigated the risks of acting with insufficient information and gave us the agility to adapt.

Reflection: This experience taught me that in situations with insufficient information, the best approach is often to prioritize flexibility and design systems that allow for iteration. By not committing fully to one architecture and instead creating a solution that could evolve, we avoided potentially costly mistakes and were able to adapt as new data became available.

2. Describe a time when you had to make a quick decision.

Situation: During the rollout of a new feature in our generative AI product at AWS, we encountered a critical issue: the API responsible for handling customer requests was suddenly experiencing significant latency, causing delays in response generation during peak hours. This was jeopardizing our service-level agreements (SLAs) with customers, and the issue needed to be addressed immediately.

Task: I had to quickly decide whether to implement an immediate rollback to a previous stable version of the API or attempt to identify and fix the issue in real-time, risking prolonged downtime and further degradation of service.

Action:

  1. Evaluate the Severity:
    • I immediately convened a war room with the engineering team to assess the situation. We reviewed monitoring data to understand the scope of the latency issue and traced it to a new optimization feature that was intended to reduce costs by batching requests.
  2. Consider Risks and Trade-offs:
    • Rolling back the API would mean temporarily disabling the new feature, which would lead to increased costs but would immediately restore performance. On the other hand, fixing the issue in real-time was risky because it could extend downtime if the root cause was not quickly identified.
  3. Decide on Rollback:
    • After weighing the risks and consulting with the team, I decided to initiate an immediate rollback. I prioritized restoring service performance to meet our customer SLAs, knowing that we could revisit the optimization feature once stability was achieved. We initiated the rollback using our CI/CD pipeline, which had a rollback mechanism built into it for precisely these situations.
  4. Post-Rollback Investigation:
    • Once the rollback was complete and performance was restored, we continued investigating the root cause of the issue. We identified that the batching mechanism introduced contention in the database during peak hours, causing the latency spikes.

Result: The rollback successfully restored the API’s performance within 15 minutes, preventing further SLA breaches. We later re-implemented the batching feature with a more robust design that included dynamic scaling to handle peak loads, ensuring that the issue would not recur. This adjustment led to a 10% cost reduction without impacting latency.

Reflection: This experience highlighted the importance of prioritizing stability and customer experience over immediate cost savings. In critical situations, quick decision-making is essential, but having a fallback plan (like a rollback mechanism) is equally important. It reinforced the value of investing in reliable CI/CD pipelines and the importance of balancing performance optimization with system stability.

3. Describe a time when you insisted on doing something, but it turned out to be wrong.

Situation: While working on a natural language processing (NLP) project in the Alexa AI team, I was convinced that incorporating a more complex deep learning model with additional layers and attention mechanisms would significantly improve query understanding accuracy. I believed that this added complexity would allow the system to capture more nuanced patterns in the data, particularly for ambiguous or rare queries.

Task: I insisted on prioritizing the implementation of this complex model over a simpler, proven model that was less computationally expensive. The task was to build and deploy this model into our query understanding system with the expectation that it would lead to noticeable improvements in accuracy.

Action:

  1. Insisting on Complexity:
    • I convinced the team to implement the more complex model, despite some reservations from a few engineers who felt that the added complexity might not justify the incremental gains in accuracy. I believed that the model’s theoretical benefits would translate into real-world performance improvements, especially for edge cases.
  2. Deployment and Evaluation:
    • We invested significant time and resources into building and training the complex model. After extensive testing and validation, we deployed it into production, expecting improvements in user query interpretation, particularly for ambiguous queries.
  3. Unexpected Results:
    • After deployment, we monitored the system’s performance and noticed that while the model did improve accuracy slightly for rare edge cases, the overall performance gains were marginal. Moreover, the added complexity significantly increased inference time and computational costs, which began to affect the overall responsiveness of the system.
  4. Reverting to Simplicity:
    • After further analysis, it became clear that the trade-offs were not justified. We ultimately decided to revert to a simpler model, which provided nearly the same level of accuracy but with much lower computational overhead. We retained the complex model for specific use cases where it excelled, but it was no longer the default approach.

Result: The decision to prioritize a simpler model improved system performance and reduced costs by 25%, while maintaining similar levels of accuracy for most queries. The more complex model was relegated to specific edge cases where its benefits were clearer, but it was no longer the default for all queries.

Reflection: This experience taught me a valuable lesson about the importance of balancing complexity with practicality. I learned that sometimes a simpler solution can provide nearly the same benefits with fewer trade-offs, and that complexity should only be introduced when the value it adds is clear and measurable. It also reminded me of the importance of listening to diverse viewpoints and being willing to adjust course when the data doesn’t support the initial hypothesis.

4. What is the mistake you’ve made or a decision in work you regret?

Situation: Early in my management career at Alexa AI, I led a project where we were tasked with significantly improving our speech recognition model’s performance. We were under pressure to deliver quickly due to external commitments, and I made the decision to prioritize speed over thorough testing in order to meet the deadline.

Task: The task was to deploy an updated speech recognition model that incorporated new training data and some architecture improvements. My decision was to fast-track the deployment to hit the deadline, reducing the amount of testing and validation normally required.

Action:

  1. Rushed Deployment:
    • I pushed the team to accelerate the timeline and deploy the model with less testing than usual, believing that the improvements we had made during development would ensure sufficient performance.
    • We skipped some of the more rigorous stress tests and edge case validation in favor of getting the product out the door quickly.
  2. Negative Impact:
    • Shortly after deployment, we began receiving reports of increased error rates in specific accents and dialects that were underrepresented in the new training data. The model’s performance degraded in these areas, leading to user frustration and a spike in customer complaints.
  3. Damage Control:
    • We had to roll back the deployment and initiate a thorough round of testing and retraining, which delayed the project significantly and eroded some of the trust we had built with stakeholders and customers. The rollback and rework ended up taking longer than if we had conducted proper testing upfront.

Result: The rollback and rework delayed the project by an additional four weeks, and while we eventually corrected the model’s performance, the rush to deploy initially caused damage to the user experience and the team’s credibility.

Reflection: This experience taught me the importance of adhering to proper testing and validation procedures, even under tight deadlines. Rushing

to meet a deadline without ensuring the quality of the product ultimately costs more in time and resources than doing things right the first time. It reinforced the lesson that quality assurance is critical and that cutting corners can have significant negative consequences, both technically and reputationally.

5. Biggest failure or most regrettable project?

Situation: One of my most regrettable projects was during my tenure at Alexa AI when we were developing a personalized news briefing feature. The feature aimed to use AI to curate and deliver personalized news updates based on user preferences and listening habits. Despite significant investment in machine learning models and engineering resources, the project ultimately failed to gain traction due to poor user engagement and misaligned assumptions about user behavior.

Task: The task was to design and deploy a personalized news recommendation system that would deliver content based on the user’s history and stated preferences. We believed that personalized, bite-sized news briefings would be highly engaging and increase user retention.

Action:

  1. Over-Focusing on Technology:
    • We focused heavily on building sophisticated recommendation algorithms that could learn from user interactions and tailor content dynamically. However, we spent less time validating our assumptions about user needs and preferences.
    • We built the product based on the idea that users wanted highly tailored, algorithm-driven news selections, without conducting enough market research or user testing to confirm this hypothesis.
  2. Launch and User Feedback:
    • After launch, user engagement with the feature was much lower than expected. Many users preferred curated, broader news rather than highly personalized snippets. Others found the personalization algorithms too narrow and felt they were missing important stories.
  3. Post-Mortem and Realization:
    • During the post-mortem, we realized that we had fallen into the trap of prioritizing technical excellence over user-centric design. We hadn’t taken the time to thoroughly understand our user base and validate whether our approach to personalization aligned with their preferences.

Result: The project was eventually shelved due to low adoption rates, despite the sophisticated technology behind it. The failure cost significant engineering resources and led to a rethinking of how we approached user-driven product development.

Reflection: This failure taught me that even the most advanced technology cannot succeed if it isn’t solving a real user problem. It reinforced the importance of validating user needs and behaviors early and often in the product development process. I now place a greater emphasis on user research and feedback loops before committing significant resources to building complex systems.

6. Tell me about a time when you were solving a problem that was ambiguous.

Situation: At AWS, I led a project to implement a natural language processing (NLP) system that could automatically categorize customer support tickets. The problem was highly ambiguous because there was no clear definition of the categories, and the data was noisy, with many tickets overlapping multiple categories or falling into none at all.

Task: My task was to develop an NLP solution that could effectively categorize these tickets despite the ambiguous and overlapping nature of the data. We needed to define categories that were useful for business operations while ensuring that the system could handle the variability in the tickets’ language and context.

Action:

  1. Define the Problem Space:
    • I organized brainstorming sessions with stakeholders from the customer support and operations teams to understand their needs and pain points. We worked to define an initial set of categories that would be useful for automating ticket triage.
  2. Data Exploration and Clustering:
    • We performed exploratory data analysis (EDA) on the ticket data to identify patterns and clusters that could inform the category definitions. Using unsupervised learning techniques like k-means clustering, we identified natural groupings of tickets based on common themes and language.
  3. Iterative Approach to Category Design:
    • Given the ambiguity of the problem, we adopted an iterative approach. We started with a simple set of categories and tested the system on real ticket data, gathering feedback from the customer support team to refine the categories over time.
    • As we processed more data, we adjusted the categories dynamically, merging similar ones and splitting overly broad ones based on ticket classification accuracy and business usefulness.
  4. Feedback Loop and Continuous Improvement:
    • We implemented a feedback loop where incorrectly categorized tickets were reviewed by human agents, and their corrections were fed back into the model. This allowed the system to improve its accuracy over time despite the initial ambiguity of the problem.

Result: After several iterations, the system achieved a categorization accuracy of over 85%, significantly reducing the workload of human agents and speeding up ticket resolution times. The iterative approach allowed us to navigate the ambiguity of the problem and develop a system that adapted to the evolving needs of the business.

Reflection: This project reinforced the importance of embracing ambiguity and using an iterative, data-driven approach to solve complex problems. I learned that in ambiguous situations, it’s often more effective to start with a simple, flexible solution and refine it over time based on real-world feedback rather than trying to design a perfect solution upfront. This has influenced how I approach problem-solving in situations where there is no clear or predefined path.

Project & Time Management:

  • Describe a time when you had a tight deadline.
  • Tell me about a time when business requirements changed, and you had to adapt.
  • Describe a time when a project took much longer than expected. How did you remedy it?
  • Didn’t finish a project on time?
  • How do you manage priorities with tight deadlines?
  • How do you and your team resolve priority conflicts?

1. Describe a time when you had a tight deadline.

Situation: At AWS, I led the development of a multimodal AI feature that needed to be delivered within a tight deadline due to a scheduled demo for a key client. The project involved integrating text, image, and voice inputs into a customer support system, and the deadline was non-negotiable because it aligned with the client’s quarterly review.

Task: We had just five weeks to build and deploy a working prototype that could handle real-time multimodal queries. This required us to design, implement, and test a complex pipeline in a significantly compressed timeline while maintaining a high standard of quality.

Action:

  1. Break Down the Project:
    • I quickly broke the project into critical components: data ingestion, multimodal feature extraction, query processing, and response generation. I assigned specialized teams to each component to work in parallel, with clear milestones and responsibilities.
  2. Set Priorities and Trim Scope:
    • Given the time constraint, I worked with the product team to define the minimal viable product (MVP) for the demo. We focused on delivering a core set of functionalities that could demonstrate the technology effectively, while deferring non-essential features to future phases.
  3. Adopt Agile Methodologies:
    • We adopted an agile approach with daily standups and bi-weekly sprints. I emphasized rapid iteration, allowing teams to focus on delivering functional increments of the system and continuously integrating their work. This helped us identify issues early and stay on track.
  4. Resource Optimization:
    • To meet the deadline, I leveraged additional resources from related projects, bringing in engineers with relevant expertise. I also worked closely with the DevOps team to automate as much of the deployment pipeline as possible, reducing time spent on manual tasks.
  5. Regular Check-ins and Escalation:
    • I maintained close communication with all stakeholders, providing transparent updates on progress and flagging any risks early. When bottlenecks arose, I facilitated cross-team collaboration to resolve them quickly, avoiding delays.

Result: We successfully delivered the prototype on time, and the demo went smoothly. The client was impressed with the multimodal capabilities, which led to an expanded contract and further investment in the project. Despite the tight timeline, the team delivered a high-quality product, and the agile methodology allowed us to adapt quickly to challenges.

Reflection: This experience reinforced the importance of clear prioritization and communication when dealing with tight deadlines. By breaking down the project into manageable components and focusing on the MVP, we were able to deliver results efficiently without sacrificing quality. I also learned the value of agile methodologies in helping teams stay focused and adaptive under pressure.

2. Tell me about a time when business requirements changed, and you had to adapt.

Situation: While working on an NLP project at Alexa AI, we were developing a system to improve query understanding for voice interactions. Midway through the project, business requirements shifted when leadership decided to expand the project’s scope to include internationalization, requiring support for multiple languages instead of just English. This significant change occurred late in the development cycle, which forced us to reassess our approach.

Task: I had to adapt our strategy to incorporate multilingual support into the query understanding system, which required reworking our models and expanding the data pipeline to handle different languages. This had to be done without delaying the original project deadlines.

Action:

  1. Evaluate Impact and Adjust Scope:
    • I immediately conducted an impact assessment to understand how this change would affect the project timeline, resources, and existing work. After determining the level of effort required for multilingual support, I worked with the product team to prioritize languages based on business needs and user demographics, focusing on high-impact languages first.
  2. Leverage Pre-built Models and Transfer Learning:
    • To save time, we decided to leverage pre-trained multilingual models, such as mBERT, rather than building language models from scratch. We also used transfer learning techniques to fine-tune these models for specific languages, significantly reducing development time.
  3. Modify Data Pipeline:
    • We expanded the data pipeline to handle multilingual datasets, integrating data augmentation techniques to improve the diversity of training data for less-represented languages. I also worked with the data engineering team to ensure that the pipeline could accommodate the additional data without compromising performance.
  4. Agile Approach to Multilingual Integration:
    • I adopted an incremental approach, integrating one language at a time into the system. This allowed us to test and validate each language independently, ensuring quality control without overwhelming the system.
  5. Resource Reallocation and Timeline Management:
    • I reallocated resources, temporarily pulling in engineers with experience in internationalization to help accelerate development. Additionally, I adjusted the project timeline to accommodate the expanded scope but ensured that the initial language (English) would still be delivered on time, with subsequent languages following shortly after.

Result: The project was successfully adapted to support multiple languages, with English launching on time and the first wave of additional languages delivered shortly afterward. The internationalization effort increased the system’s user base and contributed to a 20% increase in user engagement in non-English markets.

Reflection: This experience taught me the importance of flexibility and strategic planning when business requirements change unexpectedly. By leveraging existing resources, such as pre-trained models and transfer learning, we were able to adapt quickly without compromising quality. I also learned the value of incremental delivery in managing large scope changes effectively.

3. Describe a time when a project took much longer than expected. How did you remedy it?

Situation: I was managing a project at AWS to implement a personalization engine for an AI-powered recommendation system. Initially, we estimated the project would take three months to complete. However, the project ended up taking nearly six months due to unforeseen complexities with the integration of user data, privacy compliance issues, and the need to retrain models with new data sources.

Task: I had to get the project back on track while addressing the delays caused by data integration challenges, privacy compliance requirements, and the performance of the retrained models. This required reassessing our approach to meet the project’s goals without further delays.

Action:

  1. Root Cause Analysis:
    • I conducted a thorough review of the project timeline and identified the key issues causing delays. These included difficulties with accessing and integrating user data due to privacy concerns, model retraining that was taking longer than expected, and gaps in cross-team communication between the data engineering and AI teams.
  2. Break Down and Reprioritize Tasks:
    • I broke down the project into smaller, more manageable components, reprioritizing tasks based on their criticality and dependencies. I worked closely with the data privacy team to resolve compliance issues first, ensuring that data could be accessed and processed legally and securely.
  3. Addressing Technical Bottlenecks:
    • I allocated additional resources to the data engineering team to expedite the data integration process and ensure that the AI team could continue model retraining with clean, compliant data. We also adopted incremental retraining approaches to get intermediate models deployed while continuing to improve them over time.
  4. Enhance Communication and Collaboration:
    • I instituted weekly cross-functional meetings between the data engineering, AI, and privacy teams to improve communication and ensure alignment. This helped identify potential roadblocks earlier and allowed for quicker decision-making.
  5. Revised Timeline with Stakeholders:
    • I communicated transparently with stakeholders about the delays and provided a revised timeline with more realistic estimates. I also established clear milestones and checkpoints to track progress and ensure that any future delays were caught early.

Result: By focusing on incremental progress, resolving data privacy issues, and enhancing cross-team collaboration, we were able to complete the personalization engine within the new timeline. The system was eventually launched successfully, delivering personalized recommendations that improved user engagement by 18%. The lessons learned from this project also helped streamline future projects with more accurate timelines and better risk management.

Reflection: This experience taught me the importance of conducting thorough risk assessments early in the project lifecycle and maintaining flexibility to adjust plans as needed. It also reinforced the value of cross-team collaboration and the need to address communication gaps proactively. Delays can happen, but having a clear strategy for getting back on track is critical for long-term success.

4. Didn’t finish a project on time?

Situation: During a project at Alexa AI to enhance voice interaction personalization, we missed our initial deadline due to unexpected issues with model performance in certain user demographics, which we had not adequately accounted for during initial testing. These issues arose late in the project cycle, just as we were preparing for deployment.

Task: We needed to identify the root cause of the model’s performance issues and make the necessary adjustments, all while managing stakeholder expectations and minimizing further delays.

Action:

  1. Root Cause Analysis:
    • I worked with the data science team to conduct a deep dive into the model’s performance data. We discovered that the training data was not sufficiently representative of all user demographics, particularly those with accents and dialects that differed from the standard training set. This lack of diversity in the data led to subpar performance for certain user groups.
  2. Adjust Project Scope and Timeline:
    • I adjusted the project scope to prioritize retraining the model with a more diverse dataset. We sourced additional training data that better represented the full range of user demographics and incorporated it into the model training process.
  3. Manage Stakeholder Expectations:
    • I communicated the delay to stakeholders, explaining the reason for the missed deadline and emphasizing the importance of delivering a model that would provide a high-quality experience for all users, rather than rushing to meet the original timeline.
  4. Implement Rolling Deployment:
    • To minimize the impact of the delay, we adopted a rolling deployment strategy. We deployed the improved

model to a subset of users first, allowing us to test and validate its performance with the new data before scaling it to the entire user base. This approach allowed us to continue making progress while ensuring quality.

Result: Although we missed the initial deadline by four weeks, the improved model was eventually deployed successfully, achieving a 25% increase in personalization accuracy across all user demographics. The stakeholders appreciated the transparency and the focus on delivering a higher-quality product, and the project ultimately met its long-term goals.

Reflection: This experience reinforced the importance of thoroughly testing models across diverse datasets early in the project lifecycle. It also taught me that managing stakeholder expectations through clear communication is critical when unforeseen challenges arise. Missing a deadline can be mitigated by focusing on delivering quality and maintaining transparency throughout the process.

5. How do you manage priorities with tight deadlines?

Situation: In my role at AWS, I often managed multiple high-priority projects with tight deadlines. One such instance involved simultaneously working on a multimodal AI feature while also overseeing the deployment of an NLP model for a separate project. Both projects were critical to the organization’s goals, and I had to ensure that neither was delayed despite the competing demands.

Task: I needed to prioritize tasks effectively to ensure that both projects stayed on track without sacrificing quality. This required careful balancing of time, resources, and stakeholder expectations across both projects.

Action:

  1. Prioritize Based on Impact and Dependencies:
    • I evaluated the impact and dependencies of each task within both projects, identifying critical paths that could affect the overall timeline. Tasks that were dependencies for other teams or that had the highest impact on meeting the deadlines were prioritized.
  2. Allocate Resources Flexibly:
    • I allocated resources dynamically, assigning engineers to work on critical tasks first and then shifting them between projects as needed. For example, if the NLP project was ahead of schedule, I would reallocate those engineers to the multimodal project to keep both moving forward.
  3. Time Blocking for Focused Work:
    • I implemented time blocking within my own schedule and encouraged the team to do the same. This allowed us to focus on specific tasks for set periods of time, avoiding the distractions of multitasking. This was particularly useful when we needed to tackle complex technical challenges that required deep focus.
  4. Communication and Transparency:
    • I maintained clear communication with all stakeholders, keeping them updated on progress and any potential risks. This helped manage expectations and allowed for quick decision-making if priorities needed to be adjusted.
  5. Delegate and Empower Team Leads:
    • I empowered team leads to make day-to-day decisions about task assignments within their areas of expertise, allowing me to focus on strategic oversight and reducing bottlenecks.

Result: Both projects were completed on time and met their respective goals. The NLP model was successfully deployed with a 20% improvement in accuracy, and the multimodal AI feature was delivered with full functionality for the client demo. By carefully managing priorities and resources, we were able to meet tight deadlines without sacrificing quality.

Reflection: This experience taught me the importance of structured prioritization and effective delegation when managing multiple high-priority projects. I learned that maintaining a clear view of critical paths and resource availability, while empowering team members to make decisions, is key to delivering results on time. Effective communication and transparency are also essential for keeping stakeholders informed and aligned.

6. How do you and your team resolve priority conflicts?

Situation: While leading the Generative AI team at AWS, we frequently encountered conflicts between different projects vying for resources and attention. For example, a high-profile project to implement a new AI-driven feature for customer support was competing with an internal initiative to improve the efficiency of our data processing pipeline. Both projects had tight deadlines and were critical to the company’s goals, leading to conflicts over resource allocation and prioritization.

Task: As the team lead, I needed to mediate between the competing projects and resolve priority conflicts in a way that ensured both projects could move forward without derailing either of them.

Action:

  1. Assess Business Impact and Urgency:
    • I conducted a detailed assessment of each project’s business impact, urgency, and dependencies. I met with stakeholders from both projects to understand their objectives and the consequences of delaying one over the other. This helped me develop a clearer picture of which project had more immediate business value.
  2. Create a Prioritization Framework:
    • I implemented a prioritization framework that ranked tasks based on their business impact, urgency, and dependencies. This allowed us to make more data-driven decisions about where to allocate resources and helped remove the subjectivity from the decision-making process.
  3. Negotiate Compromises:
    • I facilitated meetings between the leaders of both projects to discuss potential compromises. We negotiated staggered timelines where high-priority tasks for one project were completed first, followed by a focused push on the other project. This ensured that neither project was completely stalled, and both could progress in parallel, albeit at different paces.
  4. Reallocate Resources and Adjust Timelines:
    • I reallocated resources dynamically based on the prioritization framework. For example, we temporarily shifted additional engineers to the customer support project during its most critical phase and then transitioned them back to the internal initiative once the immediate deadlines were met. I also worked with project managers to adjust timelines and set more realistic expectations for delivery dates.
  5. Maintain Regular Check-Ins:
    • I established regular check-ins with both teams to monitor progress and ensure that any new conflicts were addressed quickly. This also allowed us to re-evaluate priorities as new information emerged and make adjustments accordingly.

Result: Both projects were successfully delivered without significant delays. The customer support feature was launched on time and received positive feedback from users, while the internal data processing improvements were completed shortly afterward, leading to a 15% increase in system efficiency. The prioritization framework became a standard practice in the team, helping us manage future conflicts more effectively.

Reflection: This experience taught me that priority conflicts are inevitable, but they can be managed effectively with clear frameworks and open communication. By focusing on business impact and involving all stakeholders in the decision-making process, we were able to resolve conflicts in a way that satisfied both teams. I also learned that flexibility and negotiation are key when balancing competing demands on time and resources.

Handling Challenges:

  • Most challenging work or project?
  • Tell me about a time when you faced a barrier and how you overcame it.
  • How do you handle irresponsible coworkers?
  • Describe a time when you worked on a project outside of your scope.
  • How do you handle situations when what your boss asks you to do is not what you want to do?
  • What do you do when a project or task drains your energy?
  • What personality traits do you prefer to avoid in coworkers or supervisors?

1. Most challenging work or project?

Situation: One of the most challenging projects I led was at AWS, where we were tasked with building a real-time, multimodal AI system for customer support. The project involved processing multiple data modalities—text, images, and voice—and required highly optimized performance due to the system’s need to handle high query volumes with minimal latency.

Task: The task was to design and implement an end-to-end pipeline that could integrate text, image, and voice data, retrieve relevant information, and generate real-time responses—all while maintaining strict SLAs for response time and accuracy.

Action:

  1. Architecting a Complex Pipeline:
    • I spearheaded the design of the multimodal pipeline by breaking it down into several critical components: data ingestion, feature extraction, cross-modal attention mechanisms, and response generation. Each component had its own unique challenges, especially given the real-time performance requirements.
  2. Technical Challenges:
    • One of the biggest challenges was optimizing the system for low latency while maintaining high accuracy across all data modalities. I introduced a scalable vector search system using a combination of AWS Elasticsearch and GPU-accelerated vector databases to retrieve relevant documents across text, image, and voice data.
  3. Cross-Team Collaboration:
    • This project required collaboration across multiple teams—data engineering, AI, DevOps, and product management. I established regular syncs to ensure alignment on objectives and facilitated cross-team workshops to troubleshoot complex issues such as integrating multimodal data into a unified vector space.
  4. Iterative Testing and Tuning:
    • We conducted iterative testing using various A/B tests to optimize the system’s performance. One significant breakthrough came when we switched from a single attention mechanism to a hierarchical one that dynamically weighted the importance of different data modalities based on the context of the query.

Result: The project culminated in a multimodal AI system that could process queries 30% faster than the previous solution while improving accuracy by 20%. The system was successfully deployed and became a key differentiator in our customer support offerings.

Reflection: This project tested both my technical and leadership skills. It taught me the importance of breaking down complex challenges into manageable parts and leveraging cross-functional expertise to drive innovation. The success reinforced the value of persistent testing and iteration in solving difficult technical problems.

2. Tell me about a time when you faced a barrier and how you overcame it.

Situation: While leading a recommendation system project at AWS, we encountered a significant barrier related to data privacy regulations. Our model relied on user data to personalize recommendations, but new compliance regulations around data handling and user consent were introduced mid-project, threatening to halt progress.

Task: The task was to ensure our recommendation system complied with the new data privacy regulations without sacrificing the quality of our personalization efforts. This required redesigning parts of the system and implementing new protocols for data handling.

Action:

  1. Assess the Regulatory Impact:
    • I immediately convened a cross-functional team including legal, data privacy, and engineering experts to assess the impact of the new regulations on our system. We identified the specific requirements around data consent, anonymization, and data storage limitations.
  2. Redesign the Data Pipeline:
    • We redesigned the data pipeline to ensure that all user data was anonymized and encrypted both in transit and at rest. I implemented stricter access controls, using AWS KMS for encryption and ensuring compliance with GDPR and CCPA by minimizing the retention of personally identifiable information (PII).
  3. Opt-In Consent Mechanisms:
    • We introduced an opt-in consent mechanism that allowed users to control how their data was used for personalization. This required reworking the front-end interfaces and updating the backend systems to manage user preferences effectively.
  4. Testing for Compliance:
    • I led the team in conducting extensive testing to ensure that the system was fully compliant with the new regulations. We engaged external auditors to review our data handling practices, and we built automated compliance checks into the CI/CD pipeline to catch any potential violations during development.

Result: We successfully overcame the regulatory barrier and launched the recommendation system without delays. The new privacy-compliant system not only met legal requirements but also improved user trust, leading to a 15% increase in user engagement. The changes we implemented became a blueprint for future projects, allowing us to adapt quickly to evolving data privacy laws.

Reflection: This experience taught me the importance of agility and cross-functional collaboration when facing regulatory challenges. It reinforced the need to design systems with flexibility and compliance in mind, ensuring that they can adapt to new regulations without major overhauls.

3. How do you handle irresponsible coworkers?

Situation: In one of my previous roles, I worked with a team member who consistently missed deadlines and failed to communicate progress effectively. This was causing delays in a high-priority project and putting extra pressure on the rest of the team.

Task: I had to address the issue of the coworker’s irresponsibility and ensure that the project could continue without further delays. My goal was to resolve the situation in a constructive manner while maintaining team morale.

Action:

  1. Private Conversation:
    • I initiated a one-on-one conversation with the coworker to better understand the root cause of their performance issues. I approached the situation with empathy, recognizing that personal or external factors might be affecting their work.
  2. Set Clear Expectations:
    • During the conversation, I laid out the specific expectations for the project, including deadlines and communication standards. I emphasized the importance of their role in the team and the impact their actions were having on others. Together, we developed a clear plan for how they would improve their performance, including more frequent check-ins and clearer communication of their progress.
  3. Provide Support and Accountability:
    • I offered additional support by assigning a mentor within the team to help guide them through their tasks and provide regular feedback. I also set up a system of weekly check-ins to ensure that progress was being made and to provide accountability.
  4. Escalate if Necessary:
    • I made it clear that if the situation didn’t improve, we would need to escalate the issue to management. However, I positioned this as a last resort, preferring to give the individual the opportunity to correct their behavior.

Result: The coworker responded positively to the feedback and began meeting their deadlines more consistently. The extra support and accountability helped them improve their performance, and the project was able to get back on track. The team dynamic improved as a result, and we completed the project on time.

Reflection: This experience taught me the importance of addressing performance issues early and with empathy. By providing clear expectations and support, I was able to help a struggling team member improve without resorting to punitive measures. I learned that most performance issues can be resolved through open communication and structured guidance.

4. Describe a time when you worked on a project outside of your scope.

Situation: At AWS, I was leading a project to develop a multimodal retrieval-augmented generation (RAG) system when I was asked to assist with a parallel initiative that involved optimizing the underlying infrastructure for large-scale data processing. This infrastructure work was technically outside the scope of my role, but it was critical to the success of both projects.

Task: The task was to help the infrastructure team optimize data pipelines to ensure that our multimodal system could handle large-scale data ingestion without performance degradation. This required me to step outside my typical AI-focused work and engage more with infrastructure design and data engineering challenges.

Action:

  1. Dive Into Infrastructure:
    • I quickly familiarized myself with the existing data processing infrastructure, including the distributed data pipelines and storage systems that supported our AI models. I spent time with the data engineering team to understand the bottlenecks that were causing performance issues.
  2. Leverage Cross-Disciplinary Knowledge:
    • Drawing on my experience with AI and data processing, I helped the infrastructure team identify areas where we could optimize the pipelines for more efficient data handling. We implemented solutions such as data partitioning, caching mechanisms, and parallel processing to reduce latency and improve throughput.
  3. Collaborative Problem-Solving:
    • I facilitated collaborative working sessions between my AI team and the infrastructure team to ensure that our solutions were aligned and that the data pipelines could support the real-time demands of the multimodal system. We created performance benchmarks and iterated on the infrastructure design based on the results.

Result: With the optimizations in place, the data pipelines were able to handle the increased load from the multimodal AI system without performance degradation. This allowed both projects to succeed, and the improvements in infrastructure led to a 20% increase in system efficiency across multiple teams.

Reflection: Working outside of my typical scope gave me valuable insights into the importance of cross-disciplinary collaboration. I learned that stepping outside your comfort zone to contribute to other areas of a project can significantly benefit the overall outcome. This experience also reinforced my belief that a strong understanding of infrastructure is crucial for building scalable AI systems.

5. How do you handle situations when what your boss asks you to do is not what you want to do?

Situation: In one of my previous roles, my manager asked me to shift focus from an AI-driven innovation project to a more routine task involving maintenance of legacy systems. While I understood the importance of the maintenance work, I was passionate about driving innovation and felt that my skills would be underutilized in a purely maintenance role.

Task: My task was to carry out the maintenance work while ensuring that I maintained some involvement in innovation projects. I needed to find a way to balance both responsibilities in a manner that satisfied my manager and aligned with my professional goals.

Action:

  1. Understand the Rationale:
    • I first sought to

understand why my manager wanted me to focus on the maintenance work. During a one-on-one conversation, it became clear that the maintenance task was high-priority due to some upcoming regulatory audits and required my expertise to ensure compliance.

  1. Negotiate a Compromise:
    • I proposed a compromise where I would dedicate a portion of my time to the maintenance work but would also continue contributing to the innovation project. I suggested a phased approach where I could prioritize the maintenance work initially and gradually shift back to innovation once the critical tasks were completed.
  2. Effective Time Management:
    • I restructured my schedule to balance both responsibilities. I allocated time blocks for maintenance tasks during high-priority periods and set aside specific time each week to contribute to the innovation project. This allowed me to meet the expectations of my manager while still pursuing my interests.
  3. Demonstrate Value:
    • To demonstrate the value of my work on the innovation project, I regularly updated my manager on its progress and how it could benefit the organization in the long run. This helped build a case for why continued investment in innovation was important.

Result: The maintenance work was completed on time and passed the regulatory audits without issue. Additionally, by balancing my responsibilities, I was able to continue contributing to the innovation project, which later resulted in a successful product launch. My manager appreciated my ability to manage both tasks effectively.

Reflection: This experience taught me the importance of flexibility and negotiation when faced with tasks that may not align with personal preferences. By understanding the broader context and finding a way to balance priorities, I was able to satisfy both my manager’s needs and my own professional goals. It reinforced the value of communication and time management in maintaining a balance between immediate business needs and long-term aspirations.

6. What do you do when a project or task drains your energy?

Situation: While working on a large-scale data migration project at AWS, I found the work to be repetitive and less engaging than my typical responsibilities, which revolved around AI and machine learning. The task was necessary but drained my energy over time due to its monotonous nature.

Task: My task was to complete the data migration while ensuring that the rest of my responsibilities were not neglected. I needed to find a way to stay motivated and engaged despite the repetitive nature of the work.

Action:

  1. Break Tasks Into Smaller Goals:
    • I broke the data migration project into smaller, more manageable goals. This helped me track progress more effectively and gave me a sense of accomplishment as I completed each milestone. It also made the overall task feel less overwhelming.
  2. Incorporate Variety:
    • I alternated between working on the data migration project and more engaging tasks related to AI development. This allowed me to balance the less exciting work with projects that energized me. By switching between tasks, I avoided burnout and maintained my productivity across all responsibilities.
  3. Focus on the Bigger Picture:
    • I reminded myself of the importance of the data migration project for the organization’s long-term goals. By reframing the task as a critical step toward improving the scalability of our AI systems, I was able to stay more motivated and see the value in completing the work.
  4. Collaborate with Others:
    • I collaborated with other team members on the migration project, which helped break the monotony. We held regular check-ins to share progress, troubleshoot challenges, and keep each other motivated. This sense of teamwork made the task feel less isolating.

Result: I completed the data migration on time while maintaining productivity in my other projects. The successful migration laid the groundwork for future AI-driven initiatives, and my ability to stay motivated through a draining task helped ensure that other projects didn’t fall behind.

Reflection: This experience taught me the importance of finding ways to stay motivated during less engaging tasks. By breaking down the work into smaller goals, incorporating variety into my schedule, and focusing on the larger impact of the project, I was able to maintain my energy and productivity. It reinforced the idea that even mundane tasks play a critical role in achieving long-term success.

7. What personality traits do you prefer to avoid in coworkers or supervisors?

1. **Lack of Accountability:

  • I prefer to avoid working with people who do not take ownership of their responsibilities or deflect blame when things go wrong. In a high-performing team, accountability is essential, and it can be frustrating when someone is unwilling to accept responsibility for their mistakes or misses deadlines without addressing the consequences.
  1. Closed-Mindedness:
    • I find it difficult to work with individuals who are resistant to new ideas or feedback. Innovation thrives in environments where people are open to experimentation and constructive criticism, and I tend to avoid working with people who are rigid in their thinking and unwilling to adapt.
  2. Poor Communication:
    • Ineffective communication can lead to misunderstandings, missed deadlines, and friction within teams. I prefer to work with people who communicate clearly and openly, whether it’s about project updates, feedback, or potential issues. Poor communicators often create unnecessary confusion and slow down progress.
  3. Lack of Collaboration:
    • I prefer to avoid working with people who prioritize individual achievement over team success. Collaboration is key to achieving complex goals, and individuals who are overly focused on their own advancement at the expense of the team can create a toxic work environment.
  4. Negativity:
    • I try to avoid people who consistently bring negativity into the workplace. A negative attitude can be contagious and detrimental to team morale. I prefer to work with individuals who maintain a positive, solution-oriented mindset, especially when facing challenges.

Reflection: These traits can create challenges in any work environment, so I strive to foster a culture of accountability, openness, and effective communication in the teams I lead. This ensures that we can work through challenges together and stay focused on achieving our collective goals.

Career Goals & Motivation:

  • Why are you leaving your current job?
  • Why Meta?
  • What are your career goals?
  • What have you learned from different projects?
  • What is the project you want to do most in the future?

1. Why are you leaving your current job?

Reason for Leaving: I have thoroughly enjoyed my time at AWS, where I’ve had the opportunity to lead cutting-edge AI and machine learning projects. However, after several years of driving innovation in the AI space at AWS, I feel that it’s time to pursue new challenges and opportunities that align more closely with my long-term career aspirations. Specifically, I’m looking for a role where I can work on large-scale, impactful projects that push the boundaries of generative AI and multimodal systems in a different environment.

At AWS, I’ve built a solid foundation in cloud-based AI and scalable systems, but I’m seeking a new challenge where I can leverage my skills in a different context, particularly at a company like Meta that operates at the intersection of social impact, advanced AI, and large-scale user interaction. I’m eager to be part of a team that drives the future of AI in ways that influence everyday experiences on a global scale.

Reflection: While I’ve had a great run at AWS, I believe that stepping into a new environment will not only broaden my experience but also provide me with opportunities to grow as a leader and innovator in AI. I’m looking forward to bringing the expertise I’ve developed to a new organization where I can contribute to shaping the future of AI in more diverse and socially impactful ways.

2. Why Meta?

Interest in Meta: Meta stands at the forefront of developing transformative technologies that are redefining how people connect, communicate, and experience the world. I am particularly drawn to Meta’s bold vision for the metaverse and its commitment to creating immersive and seamless experiences that integrate AI, AR/VR, and multimodal technologies. I believe that Meta’s scale, innovative culture, and focus on long-term impact make it an ideal place to work on projects that push the boundaries of AI and machine learning in ways that directly influence billions of people globally.

Alignment with AI Goals: Meta’s leadership in AI research, particularly in the fields of large language models (LLMs), generative AI, and multimodal systems, aligns closely with my expertise and passion. Meta’s open research culture and dedication to advancing the state of the art in AI also appeal to me because they present an opportunity to work on groundbreaking technologies that will define the future of human-computer interaction. I see Meta as a place where I can make a significant impact while working on some of the most interesting and complex problems in AI today.

Opportunities for Growth: I am excited about the possibility of collaborating with world-class researchers and engineers at Meta to develop AI systems that can power the next generation of user experiences. I’m particularly interested in how Meta is exploring RAG pipelines, multimodal AI, and the integration of these technologies into social and communication platforms. Meta’s culture of innovation and its focus on scaling AI to solve real-world problems are the primary reasons I’m excited to join the organization.

3. What are your career goals?

Short-Term Goals: In the short term, my goal is to continue advancing my expertise in AI and machine learning by working on complex, high-impact projects that involve large-scale deployment of generative AI, multimodal systems, and retrieval-augmented generation (RAG) pipelines. I aim to further refine my leadership skills by leading cross-functional teams to build AI systems that directly enhance user experiences and solve real-world problems.

Long-Term Goals: In the long term, I aspire to become a thought leader in the AI industry, particularly in the areas of generative AI, multimodal systems, and AI for social good. I want to contribute to the development of AI systems that not only push the boundaries of technology but also have a positive and meaningful impact on society. I envision leading a large AI organization that focuses on innovation at scale, helping to shape the future of AI-powered interactions in ways that are inclusive, ethical, and transformative for diverse populations.

Vision: Ultimately, my career goal is to be at the forefront of shaping the next generation of AI technologies that can augment human potential and transform industries ranging from healthcare to education to entertainment. I’m passionate about working on AI systems that bring tangible benefits to people’s lives and that help bridge the gap between cutting-edge research and real-world applications.

4. What have you learned from different projects?

Learning from Multimodal AI Projects: From leading multimodal AI projects, I’ve learned the importance of designing flexible architectures that can handle diverse data types (text, images, voice) while maintaining high performance and scalability. These projects have taught me how to integrate complex cross-modal attention mechanisms and optimize pipelines for low latency in real-time applications. I’ve also learned that collaboration across data science, engineering, and product teams is critical for successful deployment.

Learning from Personalization Systems: Working on personalization systems has deepened my understanding of user behavior and the intricacies of creating AI models that can adapt to individual preferences. I’ve learned the importance of leveraging user data responsibly while balancing personalization with privacy and compliance requirements. These projects have taught me that effective personalization requires constant iteration, data-driven insights, and a strong feedback loop from users.

Learning from Regulatory and Compliance Challenges: In projects that involved navigating regulatory and compliance challenges, I learned the critical role that data privacy, security, and ethical AI considerations play in the development of AI systems. These experiences have underscored the importance of designing AI models and data pipelines that are both compliant with evolving regulations (like GDPR) and transparent in their operations.

Learning from Cross-Team Collaboration: From cross-functional projects, I’ve gained a deep appreciation for the value of diverse perspectives and expertise. I’ve learned that successful AI projects often require collaboration with product teams, legal experts, and data engineers, in addition to AI researchers and developers. This interdisciplinary approach ensures that we build solutions that are not only technically sound but also aligned with business objectives and user needs.

5. What is the project you want to do most in the future?

Future Project Vision: One of the projects I am most excited to work on in the future is the development of advanced AI systems that can seamlessly integrate into immersive environments, such as the metaverse. I envision creating generative AI models that not only interact with users through natural language but can also understand and respond to multimodal cues (e.g., visual, spatial, and contextual inputs) in real time. The goal would be to build AI systems that enhance virtual experiences by providing personalized, dynamic interactions that feel natural and intuitive in a fully immersive environment.

Focus on Human-AI Collaboration: I want to work on AI that goes beyond just performing tasks for users—AI that can collaborate with users in creative and meaningful ways. This could involve developing AI companions that assist with learning, creativity, and even social interaction within virtual environments. I’m particularly interested in exploring how AI can augment human creativity, helping users generate content (e.g., art, stories, music) while responding to their preferences and style.

Ethical and Inclusive AI: I am also passionate about ensuring that the AI systems we build are inclusive and equitable. A future project I would love to lead involves creating AI that works for diverse populations, accounting for different cultural, linguistic, and accessibility needs. This would involve working closely with ethicists, designers, and researchers to ensure that AI technologies are developed in a way that benefits everyone, not just a select few.

Reflection: This project aligns with my broader goal of advancing AI systems that have a positive impact on society while pushing the boundaries of technology. By working on projects that merge generative AI, multimodal inputs, and immersive environments, I can contribute to shaping the future of human-computer interaction in a way that is innovative, inclusive, and transformative.

Collaboration & Team Dynamics:

  • How do you collaborate with different teams?
  • When do you need help from other teams?

1. How do you collaborate with different teams?

Approach to Collaboration:

Cross-Functional Collaboration:
I believe successful collaboration starts with clear communication, mutual respect, and a shared understanding of goals across teams. In my work at AWS and Alexa AI, many projects required collaboration between different disciplines, such as machine learning engineers, data scientists, DevOps, product managers, and legal or compliance teams. Here’s how I approach collaboration with different teams:

  1. Aligning on Objectives:
    At the start of any cross-functional project, I prioritize aligning the objectives of all involved teams. This often involves organizing a kick-off meeting where we define the project’s overall goals, each team’s role, and specific deliverables. For example, when we were building a multimodal RAG pipeline at AWS, I held workshops with the data engineering, machine learning, and product teams to ensure we all had the same understanding of what success looked like and the constraints each team had to work within.

  2. Regular Syncs and Communication:
    I establish regular sync meetings with key stakeholders from different teams. These meetings serve as checkpoints where we review progress, identify blockers, and adjust timelines or strategies as needed. I make sure these meetings are structured and time-efficient to respect everyone’s bandwidth. During the development of AI-driven personalization features, for instance, I held bi-weekly syncs with the data privacy and legal teams to ensure that our models adhered to evolving compliance standards.

  3. Creating a Collaborative Environment:
    I encourage open communication and foster an environment where teams feel comfortable sharing their challenges and insights. By creating shared Slack channels or using tools like Jira and Confluence, I help streamline communication and ensure that everyone stays informed. This approach minimizes misunderstandings and keeps projects moving smoothly, even when teams are working on different aspects of the solution.

  4. Empathy and Mutual Support:
    I make it a point to understand the challenges other teams face, whether it’s technical constraints, resourcing issues, or competing priorities. I encourage a culture of mutual support, where we look for ways to help each other meet deadlines or overcome challenges. For example, during a project to integrate an AI system with legacy infrastructure, I allocated extra engineering resources from my team to help the DevOps team optimize their deployment pipeline, ensuring that our AI models could be scaled efficiently.

  5. Clear Documentation and Handoffs:
    To avoid ambiguity and ensure smooth handoffs between teams, I prioritize clear documentation. This includes technical specs, data pipelines, and timelines. When we were working on a large-scale recommendation system, we created comprehensive handoff documents between the machine learning team and the DevOps team, outlining the deployment process, model requirements, and monitoring needs to ensure a seamless transition to production.

Example:
During the development of a personalized AI-driven recommendation system, I collaborated closely with the product, data engineering, and compliance teams. While the machine learning team focused on building recommendation algorithms, the data engineering team was tasked with ensuring that the data pipeline could handle large-scale real-time processing. I facilitated weekly syncs to ensure that data dependencies were met, and I worked directly with the compliance team to integrate data privacy measures into the model’s design. This cross-team collaboration resulted in a successful launch of the system, improving user engagement by 18%.

Reflection:
Collaboration across teams requires a balance of leadership, empathy, and communication. By maintaining clear goals, ensuring frequent updates, and fostering a culture of support and transparency, I can navigate the complexities of cross-functional projects successfully. It’s essential to approach each collaboration with the mindset that every team is an integral part of the project’s success.

2. When do you need help from other teams?

Recognizing the Need for Help:

When Special Expertise Is Required:
I recognize the need for help from other teams when a project requires specialized knowledge or resources beyond the scope of my team’s expertise. For example, when we were working on a voice processing feature for a multimodal AI system, I sought help from the Alexa AI voice recognition team, who had deep expertise in speech-to-text technology. Collaborating with them allowed us to integrate advanced voice features that were beyond my team’s core skill set.

Example:
During the development of a multimodal RAG pipeline at AWS, we needed to integrate voice inputs alongside text and image data. My team had expertise in NLP and computer vision, but we needed help from the Alexa AI team, who specialized in speech recognition and query understanding. I reached out to their team, set up a collaboration framework, and worked closely with them to ensure that the voice data was processed effectively and integrated seamlessly into our pipeline.

When Scalability or Infrastructure Challenges Arise:
I seek help from the DevOps and infrastructure teams when projects require scaling beyond the capabilities of our initial architecture. For example, while developing a recommendation system, we encountered bottlenecks with real-time data processing due to infrastructure limitations. I reached out to the DevOps team to optimize the data pipelines and ensure that our system could scale to meet the growing demands. This collaboration helped resolve performance issues and allowed us to handle a 3x increase in traffic.

When Compliance or Legal Expertise Is Needed:
I seek help from legal or compliance teams when projects involve sensitive data, user privacy, or evolving regulations. In projects that involve personalization or user data, such as a recommendation system, I work closely with compliance teams to ensure that all data handling adheres to legal requirements like GDPR. Their guidance helps us avoid compliance risks and ensures that our AI models are developed and deployed responsibly.

Example:
In one project at AWS, we were developing a system that required collecting and processing user data to deliver personalized recommendations. Midway through, new data privacy regulations were introduced. I immediately engaged the compliance team to help us understand the implications of these regulations and adjust our data handling processes accordingly. Their input was critical in ensuring that we met the necessary legal standards without compromising the effectiveness of the recommendation system.

When Cross-Disciplinary Knowledge Is Beneficial:
I seek help from product managers, UX designers, and other cross-disciplinary teams when a project needs to align technical execution with user experience or business goals. For example, in AI-driven projects, I often collaborate with product managers to ensure that the models we develop are aligned with the product’s goals and deliver real value to users. Similarly, I work with UX designers to ensure that AI-driven features integrate smoothly into the user interface and provide an intuitive experience.

Example:
While working on an AI-powered customer support system, I collaborated with the product and UX teams to refine the AI’s responses based on user feedback. The product team provided insights into user behavior, and the UX team helped design interfaces that made interacting with the AI intuitive and user-friendly. This collaboration improved the overall user experience and increased customer satisfaction by 20%.

Reflection:
Knowing when to seek help from other teams is critical for delivering high-quality results and avoiding pitfalls. I believe in fostering a collaborative culture where expertise and resources are shared across teams to solve complex challenges more efficiently. Each team brings unique strengths, and by leveraging them, we can drive innovation and ensure that projects meet their full potential.

Interview Timeline:

  • 3/5: Initial recruiter call.
  • 3/14: Phone screen (60 minutes)
    • Structure: Behavioral questions (BQ) + 2 coding problems.
    • Interviewer: Asian, unsure of the exact origin, possibly a simplified version of a name like “Sansaner.”
    • Difficulty: Medium level (need to clarify this with the interviewer).

Virtual Onsite (VO):

  • 4/5: VO interview, multiple rounds.

Round 1:

  • 2 Coding Questions:
    1. The first question seemed to involve sorting an array where each element is no more than k positions away from its sorted position.
    2. Unable to find the exact question number, but it revolved around efficient array sorting.

Round 2:

  • 2 Coding Questions:
    • The interviewer, who seemed to be Chinese, implemented the breadth-first search (BFS) method first and followed up with a depth-first search (DFS) method.
    • A simplified problem without leaving the tree; the goal was to return whether the subarray sum equals the target.

Round 3:

  • System Design:
    • Task: Design a streaming service.
    • Requirements: Must support video playback, recommendation systems, and subscriptions.

Round 4:

  • Behavioral Questions:
    • The requirements for different levels vary, but generally, it’s important to carefully prepare using their prep document.
    • For preparation, the STAR (Situation, Task, Action, Result) format is recommended.

E6 Level Expectations:

  • Leadership: Demonstrate influence on important directions for large projects and mentoring others.
  • Project Management: Show your ability to manage projects with large scopes, ideally cross-functional, and how you handle people management skills.

Round 5:

  • System Design:
    • Another system design round, focusing on scaling and architecture.

This summary should help you reflect on your experience and prepare others who are going through a similar process. Let me know if there’s anything you’d like to elaborate on!

  • 2 coding, 2 sys des, 1 BQ

  • Coding Round 1:
  • Problem: Efficiently compute the geometric median of an array of numbers.
  • Approach: Focus on efficient algorithms for optimization.
  • Coding Round 2:
  • Problem: Get the sum of all left tree leaves.
  • Approach: Tree traversal techniques, ensure correct identification of left leaf nodes.
  • Design Round 1:
  • Problem: Ads real-time aggregation system.
  • Objective: Design a scalable, efficient system for real-time ad data aggregation.

Meta - Integrity

  • About 3B users
  • Values: Authenticity, Safety, Privacy, Dignity
  • Also, need to mitigate negative experiences from non-violating content (stemming from FPs)
  • Solutions:
    • Human-in-the-loop
    • Frequent updates to training data and models
    • exponential decay or sliding window of data
    • Metrics for ROI of infra costs associated with frequent refresh (Impact of freshness on AUC-RoC)
    • Creative data augmentation: LLM based, MT based, image transformation
    • Few shot learning for less data reliance
    • Augment w/ behavioral signals (implicit)
  • Harmful is not binary, its a regression
    • Personalized harmful content (raw meat offensive to some, not to others), personalized bad experience detection models
    • 3 part content enforcement approach:
      1. Remove content that goes against FB policies
      1. Reduce distribution of problematic content
      1. Inform when content is sensitive and add a warning
  • Inventory maintenance: down ranking,space out sensitive posts, introduce friction when sharing harmful content (ask are you sure?)
  • Generative AI for Integrity Defense:
    • Few/zero shot integrity content classification, bootstrap new models
    • LLM assisted data augmentation for content classifiers
    • LLM in the loop for review of content
    • CT^2, Visual Paraphrasing

Integrity team notes

  • In domain design: full recommendation stack, goals and tactics - integrity is doing right. how to build system to tackle those integrity outcomes

  • Read Pratyusha’s publication, fb community standards. Meta strategy how it deals with problematic content

  • Technical rounds - be direct, structure, depth in all areas (hand wavy vs underlying concepts), core fundamentals, applied experience (metrics, testing paradigms, A/B testing) vs academic

  • Management - be truthful, scenario based, be for real, dont fake. depth of what you did, how did you solve the problem.

  • Timeliness (get agenda for meeting) how many questions for both coding/behavioral

  • competiting offer, timeline
  • Motivated, make a decision soon
  • Talked to pratyush and she’s interested

  • She said she has a timeline as well

  • EM vs TLM 2 - rounds, comp

How to Implement It Specifically?

Step 1: Clarify the Core Problem

  • Not every interviewer can ask questions in a clear way. Some interviewers are not good at it and cannot figure out the logic of the questions themselves. When you encounter a vague question, you should clarify the core issue as soon as possible. Abstractly, what is the available information/input? What is the required output? Is this a classification problem, regression, or relevance/matching/ranking? Once you have clarified the core issue, you can determine what type of model you need to train, and the entire pipeline will flow out easily.

Step 2: Draw a Diagram on the Whiteboard

  • A workflow with a logical relationship between the front and the back can best demonstrate the breadth of your thinking. After clarifying the core issue, before analyzing the model in detail, draw the general framework of the solution on the whiteboard. The purpose is to make the entire explanation process logically clear. According to the logical sequence, the typical solution logic includes these major parts: training/testing data, input representation, model, output, evaluation, optimization (parameter estimation). I usually start with the model, with a box in the middle, which is the core. Then draw the upstream and downstream. Here, just set up the framework and tell the interviewer that I will talk about these contents. The interviewer will be mentally prepared and can start listening to your lecture.

Step 3: Discuss the Model

  • Why do I use the word “discuss”? Because those who can seriously pass the design exam are not entry-level. For more senior people, the best atmosphere of the interview is not a simple Q&A but rather a discussion where you explain everything you know, and the interviewer sees if there is anything else they want to hear. You should interact with the interviewer during your speech. Watch their reaction—where they frown, where their expression is not relaxed—you should stop and ask, “Is there anywhere that you want me to talk more?” This gives the interviewer a chance to express themselves and helps you better address their test points.

  • In terms of the model, according to the type of task, propose which models can be used and name all you can think of. Choose 2-3 commonly used ones, compare the advantages and disadvantages, and then choose one that everyone often uses. Different models may have different inputs and outputs. So, after deciding on the model, other components will naturally emerge. In this step, you need to list the key components in your model frame and explain the relationship between them. To analyze the pros and cons of each model, you may need to draw additional model visualizations next to it. For example, when it comes to DNN, you can draw a few layers of multi-perceptron layers and mention SGD and ADAM. When it comes to using logistic regression for classification, you can write about log-likelihood to show that you also understand optimization. When it comes to regularization, you can write about L1 norm and L2 norm. This step is mainly used to show your depth. Sometimes, the interviewer will tell you the model they want to use, and you can follow their instructions. You can also decide on a model based on your own experience after explaining the pros and cons of several models.

Based on the logical flow of the steps you’ve outlined so far, Steps 4 and 5 could be as follows:

Step 4: Discuss Data Preparation and Feature Engineering

  • Data Preparation: Explain how you would handle the data before feeding it into the model. This includes data cleaning, handling missing values, normalizing or standardizing data, and any other preprocessing steps. Discuss the importance of ensuring that the data is representative and clean to avoid introducing bias or noise into the model.

  • Feature Engineering: Highlight the importance of feature engineering in improving model performance. Discuss techniques such as feature selection, creation of new features, dimensionality reduction, and how you might use domain knowledge to inform these choices. Explain how good feature engineering can lead to better model performance and why it’s crucial to this step of the design process.

Step 5: Discuss Model Validation and Testing

  • Model Validation: Explain how you would validate the model to ensure it generalizes well to unseen data. Discuss different validation techniques such as cross-validation, split validation, or using a validation set. Talk about how to avoid overfitting by using techniques like regularization or dropout in neural networks.

  • Model Testing: Detail how you would test the model after it has been trained. Discuss the importance of using a separate test set that the model has never seen during training or validation. Mention how you would analyze the model’s performance on this test set using the metrics discussed in Step 6 (e.g., precision, recall, accuracy). Also, consider any edge cases or potential failure modes the model might encounter in real-world scenarios.

These steps would fill in the gaps between discussing the model itself (Step 3) and evaluating its performance (Step 6), providing a complete, end-to-end explanation of the machine learning design process.

Step 6: Evaluation

  • Evaluation is easy to explain, and the focus is on metrics. There are three main parts: one is the ROC/AUC curve, the second is domain-specific metrics such as CTR for advertising, and the third is the confusion matrix. The focus is on the metrics that are important to your solution, such as precision/recall/accuracy.

Finally, let me talk about a few bonus points:

  • Skilled explanation of parameter estimation can show a solid mathematical background. Talk about which optimization methods can be used to estimate parameters (MSE, log-likelihood + GD, SGD - training data is too large, ADAM - sparse input) and compare the advantages and disadvantages.
  • Take the initiative to explain each part of the solution logic, especially the aspects you are familiar with because each part is very important. Don’t ask the interviewer if they want to hear you unless they explicitly stop you from talking (if the interviewer says you don’t need to talk, you lose an opportunity to show yourself). The correct way is to lead the conversation, draw a block diagram on the whiteboard, and tell the interviewer that you want to talk about XXX. After you finish talking about the entire design, ask the interviewer: “Is there anywhere that you feel I missed?”

Ask for more rice, wait for the rice to be posted—giving me rice will not reduce your own rice, it’s a small effort, mutual benefit!

The last but not the least:

  • Confirm each step with the interviewer as soon as possible, move on, and don’t waste time. You want to convince the interviewer within 45 minutes—there are so many components, and each has very little time.

This version should be much easier to read and understand.

This chat focuses on your past experience there’s not much you need to do to prepare. However, it would be advisable to pull your thoughts together ahead of time, and here’s the structure you can expect:

  • Experience - covering the type of roles you’ve held, the breadth and depth of responsibility, and size of projects managed. This is also your opportunity to showcase your content expertise in your field of work
  • Technical breadth and depth - At FB, we emphasize collaboration across multiple teams and individuals, so be able to talk about how your work has spanned multiple teams and/or iterations
  • TL (project management) skills - including technical mentoring – think about your role in the setup, execution and delivery of a project
  • People management skills - including upward influencing, mentorship, empathy, people growth etc.
  • Agility - Indicator of your growth opportunities, as measured through your capability and willingness to absorb knowledge from your experience, and use these lessons to become even more effective in new and different situations
  • Motivation - What would you be interested in working on at Facebook, or why are you interested in working to make Facebook better? Do you exhibit a strong general drive and desire to progress as a leader?

  • Meta/ things: IR, DPR, Llama 2, GenAI, LoRA

  • Also, don’t be afraid to be vulnerable and talk about difficult subjects, show senior level leadership qualities as this is a senior role.

GenAI 1point3acres

Certainly! Here’s the text with added spaces for readability:

GenAI 1point3acres

  • Handwritten logistic/linear regression forward and backward calculation + common optimizer
  • Manual data manipulation Hand-made simple machine learning pipeline (data + model + verification) - baidu 1point3acres Hand-made simple model (LR forward and reverse)
  • Design a personalized location recommendation system
  • Testing regularization, evaluation metrics
  • Basically, it is to explain the process of recommendation from candidate generation to ranking which is e5
  • What I encountered was to write an online code to calculate the mean and variance and a calculation about average precision AUC questions Hello! The content hidden in this post requires a score higher than 188 to be viewed. Your current score is 0. Use VIP to instantly unlock reading rights or view other ways to earn points.
  • The first question is to give you a list such as and find the sum. For each number in this array when calculating the sum, it must be multiplied by the series of the array. For example, the sum of the example array = 11 + 21.
  • Design a harmful content detection system. Can you share the focus process? Thanks (check dataset).
  • The meta research scientist in the interview machine learning store: Likou wants a long time logistics. The first question used BFS and later the interviewer was asked to use DFS. VO: a total of four rounds 2 coding + ML design + BQ advertise. In the first round of ml design, a Korean girl designed a Facebook event predictor. I asked how to collect data, how to do feature engineering, what model to use, how to evaluate, how to improve, and some basic ML questions. In the second round of coding, I encountered an unreliable person.
  • Frequently asked questions: Write K-means, write knn, implement a conv2d, write code to calculate precision, recall.
  • If it is difficult, look at the pseudo code of the paper and write a python code to implement it. It simply allows you to write a linear regression and knn or something. I have seen similar ones in the field. You can search for them.
  • Implement batch norm.
  • The two questions I encountered are: 1. Top K frequent words (file reading and writing, a common follow-up is what if the file is too large and cannot be read at once. Since I have already considered it when stating the solution. So the interviewer didn’t ask about follow-up and asked me to analyze the complexity and correctness). 2. Generate mines, follow-up is minesweeping (the first question is very simple, the second question is lc529 classic BFS).
  • Yi Erjiu Liu Shenliu (Follow up: how to detect invalid begin/end time) notification pass in the morning and afternoon.
  • ML design: Design a classifier for bad ads.
  • BQ: A time you had conflict with others, most challenging.
  • The focus is on how to train a speech translator. It is probably the questions that a general train model will ask: data, model, loss, evaluation. But I am not familiar with this field so I feel that I can’t answer it well. Then I read the interviewer’s questions. Through experience, I found that the researcher from FAIR is doing research in this area. I guess NLP is very interested in the background of the interviewers, so if you know the name of the interviewer, it is best to look for it and have a preparation direction. I interviewed E5 but was down-leveled. I’m looking to see if I can earn it back.
  • They were basically simple ML foundation things such as which metric evaluation to choose, the reasons for dropout in NNet, etc.
  • The Indian lady was more than ten minutes late, so she only took one question: merge intervals from two sorted arrays O(n) time complexity. VO: ML system design: Design a simplified ads ranking system; 1 billion daily active users, 2 ads shown to customer. It involves how the specific system pipeline is set up and what components are there. ML research: For Facebook marketplace, how to categorize customers’ posts based on the image and text information.
  • Supplementary round of extra noodles ML design: Instagram newsfeed ranking system.
  • My understanding is that this user scale needs to use load balancer or Kubernetes to handle user requests. 2 ads shown per user should not care much about pairwise ranking order and need to take the two with the highest absolute value. Ads candidates are also large scale modeling wise, definitely use the funnel approach. Basically, we asked about everything from system pipeline to model to feature and data sample. ML research is about how to process image and text data for classification, not user content recommendation.

Interview questions from earlier

  • How to design LLM end to end, query -> song return
  • Read Eugene Yan LLM
  • Gen AI text diffusion
  • prompt, what metadata
  • Equation hallucination
  • Fine tuning Bert two losses
  • Losses evaluation
  • Mean average precision at k
  • Papers personalizatiom recommender system
  • Amazon llm articles
  • How do you measure diversity
  • do you finetune the LLM or not
  • Hallucination vs CT^2

Experience

Amazon Music

  • Currently, at Amazon, I lead the Music team for query understanding (map to standard functions, play Music) (finetuned an LLM input text, output is the API call. Trained base LLM, finetuned with API data with human annotations) and personalization.
    • So as a user interacts with an Alexa device, say they say “play music”, we need to help understand the request and personalize it with the customers detail.
  • Read LLM + Recsys

Alternative use Gorilla esque for LLM to output API

  • User prompts
  • document with API documentation retrieved via BM25
  • Added with the prompt
  • outputs the API

Finetuning LLM process

  1. Data Collection:
    • Gather labeled data: Assemble a dataset where each input represents a user’s music-related query, and the corresponding output is the appropriate API function, such as playMusic() or playLatestMusic().
    • Data augmentation: Increase the variety of your dataset by rephrasing music requests, introducing typical misspellings, or using diverse ways to request music. Fine-tuning a Large Language Model (LLM) like GPT-3 or GPT-4 to emit or generate APIs would require a dataset that contains descriptions or prompts along with their corresponding API structures or responses. The dataset should cover a wide range of use cases, contexts, and scenarios to ensure that the LLM can generalize well to different API requirements. Here’s an illustrative breakdown of what such a dataset might look like:

  2. Dataset Structure:

    The dataset would ideally be structured with input-output pairs, where the input is a description or a task, and the output is the corresponding API structure or response.

    {
    "input": "Create a REST API endpoint to retrieve user information based on user ID.",
    "output": "GET /users/:id"
},
{
    "input": "Design an API that allows updating a product's price in an e-commerce system.",
    "output": "PUT /products/:productId/price"
},
...

  3. API Types:

    Different types of APIs should be covered:

    • RESTful APIs: Emphasizing different HTTP methods like GET, POST, PUT, DELETE.
    • GraphQL: Queries, mutations, and subscriptions.
    • Websockets: How to set up a connection or how messages are sent/received.
    • RPC: Procedure calls for certain functions.

  4. API Elements:

    The dataset should cover different elements associated with APIs:

    • Endpoints: Different paths, route parameters.
    • Headers: Authorization, content type, etc.
    • Parameters: Query parameters, body parameters.
    • Responses: Status codes, response bodies, error messages.

  5. Complex Scenarios:

    It’s not just about endpoints, but also about combining different elements:

    {
    "input": "Design an API to upload an image, ensure it's in PNG format and the size should not exceed 5MB.",
    "output": "POST /upload/image\nHeaders: Content-Type: image/png\nConstraints: Max size 5MB"
}

  6. Real-world Examples:

    Include real-world scenarios and existing API documentation. This helps the model understand commonly used patterns and best practices in API design.

  7. Contextual Cases:

    The dataset should also contain examples that require a deeper understanding of context:

    {
    "input": "Create an endpoint for a blogging platform to allow authors to save drafts.",
    "output": "POST /blog/drafts"
}

  8. Validation and Errors:

    Incorporate examples that handle validation, error messages, and other edge cases:

    {
    "input": "Design an API to register a user. Ensure the email is valid.",
    "output": "POST /users/register\nParameters: {email: 'valid-email@example.com', password: 'string'}\nErrors: {status: 400, message: 'Invalid email format'}"
}

  9. Versioning:

    Incorporate examples that demonstrate versioning in APIs:

    {
    "input": "Introduce a version 2 for retrieving user data which also includes their purchase history.",
    "output": "GET /v2/users/:id/purchase-history"
}

  10. Natural Language Variability:

    For better generalization, include various ways of phrasing the same requirement. This ensures that the model can understand diverse prompts.

  11. Annotations:

For advanced fine-tuning, you can annotate the dataset to specify parts of the API, like “endpoint”, “method”, “parameter”, etc.

To fine-tune an LLM effectively, this dataset should be sufficiently large and diverse. Once the model is trained, rigorous testing and validation are necessary to ensure the generated APIs are accurate, feasible, and secure.

  1. Preprocessing:
    • Tokenize: Break down music queries into tokens, which can be words or subwords.
    • Contextualize: Take into account prior context. This might include details like the last song the user listened to or their current mood.
    • Use NER: Extract specific entities like song titles, artist names, or genres from the user’s query using Named Entity Recognition. This will help in better understanding and categorizing user requests.
  2. Fine-tuning:
    • Set up the model: Start with an LLM that has pretrained weights.
    • Define a task-specific head: For this job, you’d probably want a classification layer where each class matches a different API function.
    • Train: Use your music dataset to adapt the model. Adjust settings, like learning rates and batch sizes, when needed.
  3. Evaluation:
    • Validation: Throughout training, check how well the model is doing using a separate validation set.
    • Testing: After the fine-tuning is done, evaluate how well the model understands music-related queries using a distinct test set.
  4. Deployment:
    • Once you’re sure the model is reliable, add it to your system. Now, it will figure out user’s music wishes and trigger the right API calls, like playMusic() or playLatestMusic().
  5. Feedback Loop:
    • Regularly get feedback on the model’s real-world performance in interpreting music requests.
    • Update the model using new data from time to time. This keeps its performance high and helps it stay in tune with changing music tastes or user behaviors.
  • Important Points to Remember:
  • Compute and Storage Costs: Think about the amount of computer power and storage you’ll need, both for training and for using the LLM.
  • Ethical Matters: Make sure your data respects privacy rules. And aim to reduce any biases in the model, even those related to music.
  • Versioning: When you make updates, keep track of model versions. This way, you can go back to an older one if a new version causes problems.
  • With an LLM that’s been fine-tuned this way, users can tell the system about their music choices in a more natural way. In turn, the system can figure out what they mean and play songs or offer a music experience that fits them just right.
  1. Validation
    • Validate against a schema
    • Confidence score

Finer details

In building a music recommendation and playback system:

  1. Entity Recognition: The system identifies key details like song names, artist names, and genre to decide the appropriate playlist or station, ensuring a range of songs rather than just one.

  2. Intent Classification: It determines user’s request type, e.g., general music playback like “play songs by Adele” versus specific requests such as “play Adele’s latest music.”

  3. Context Understanding: Factors such as user’s location, time, holidays, and content preferences (like explicit content) are considered.

  4. Process Overview:

    • Intent Recognition: Determines the primary user action, like “play music.”
    • Slot Filling: Extracts details like song (“Hello”), artist (“Adele”), playback device (“living room speaker”), and volume (“60%”).
    • Argument Building: Uses extracted details to form function arguments, e.g., track="Hello", artist="Adele".
    • Query Resolution: The system matches the intent and details to an API function: playMusic(track="Hello", artist="Adele", device="living room speaker", volume=60).
    • Handling Incomplete Queries: If a query lacks details, the system asks follow-up questions, like clarifying the artist for a song title.
    • Execution: The determined function is triggered, initiating the playback or other actions.

Evaluation

Evaluating the fine-tuned LLM for music intents requires a comprehensive approach that ensures not only its technical performance but also its usability and relevance to users. Here’s a structured plan:

  1. Quantitative Metrics:
    • Accuracy: Calculate the percentage of user queries that the model classifies correctly into the intended API functions like playMusic() or playLatestMusic().
    • Precision, Recall, and F1-score: Especially important if there’s a class imbalance in the API functions. For instance, if users more frequently request to play music than to play the latest music.
    • Confusion Matrix: Understand which categories or intents are commonly misinterpreted.
  2. Qualitative Analysis:
    • User Testing: Engage a diverse group of users to interact with the model in a real-world setting. Gather feedback regarding its accuracy, relevance of music choices, and overall user satisfaction.
    • Error Analysis: Manually review a subset of misclassifications to identify common themes or patterns. This might reveal, for instance, that the model struggles with recognizing certain genres or artists.
  3. Real-world Performance Metrics:
    • Engagement Metrics: Monitor how often users engage with the music played. A decrease in skips or an increase in full song plays can be indicators of good recommendations.
    • Retention Rate: Measure how often users return to use the recommendation feature. A higher return rate can indicate user satisfaction.
    • Feedback Collection: Allow users to provide feedback directly (e.g., “this wasn’t what I was looking for”) and use this feedback to iteratively improve the model.
  4. NER Evaluation:
    • Entity Recognition Accuracy: Since NER is used in preprocessing, measure how accurately the model identifies and categorizes entities like song titles, artist names, or genres.
    • Coverage: Determine the range of entities the model can recognize. It should ideally recognize a wide array of songs, artists, and genres without significant gaps.
  5. Usability Testing:
    • Intuitiveness: Gauge how easily users can formulate queries and if the system’s responses align with their expectations.
    • Response Time: Since it’s a real-time recommendation system, the model’s response time should be quick to ensure a seamless user experience.
  6. A/B Testing (if possible):
    • Comparison with Baseline: Compare the LLM’s performance against a baseline system (perhaps the current system in use or a simpler recommendation model). By randomly assigning users to interact with either system, you can measure differences in user engagement and satisfaction.
  • In essence, using the LLM, you’re dynamically translating natural language instructions into structured function calls that the system can understand and act upon. This approach makes interactions intuitive for users while ensuring precise actions on the backend.
  • It’s about gauging both the implicit and explicit needs and delivering a seamless music experience.
  • Our team’s focus is around customer growth so we serve recommendations that will help grow our customer base
    • This includes, Next Best Action via Multi-armed bandit, where we look to educate inactive users by giving them 3 personalized push notifications, prompting them to perform different actions on the app.
      • The number 3 was decided after several experimentation where we didn’t want to bombard the user but still educate them
    • We also have a partnership with Amazon.com retail where we find correlation between retail products and music latent factors and have it on the Amazon.com page item to item

NuAIg

  • Spinoff from Oracle in the healthcare domain automating administrative and operational task
  1. Creating a Clinical Documentation Tool:
    • Named Entity Recognition (NER): To identify specific entities in the text, such as patient names, medication names, diseases, procedures, dates, and other relevant medical terms.
    • Information Extraction: Beyond just recognizing entities, this task involves extracting relationships and attributes associated with these entities. For instance, understanding that a specific drug was prescribed for a particular symptom or disease.
    • Text Classification: To categorize different parts of a clinical note (e.g., diagnosis section, treatment section, patient history).
    • Topic Modeling: To automatically identify the main topics covered in a clinical note, aiding in quick summarization.
  2. Designing an Information Retrieval System: –> FAISS
    • Document Indexing: Efficiently indexing medical guidelines, patient data, and treatment options for rapid retrieval.
    • Query Understanding: Interpreting what a user (possibly a healthcare professional) is looking for, even if their query is in natural, conversational language.
    • Document Ranking: Sorting the retrieved documents by relevance based on the user’s query and possibly other factors like patient specifics.
    • Semantic Search: Using embeddings and other advanced techniques to ensure the retrieval system understands the meaning and context, not just keyword matches.
  3. Automating Claims Processing:
    • Named Entity Recognition (NER): As mentioned earlier, this would be used to identify specific entities like patient names, diseases, treatments, amounts, dates, etc.
    • Text Classification: To categorize different sections of the claim form or to determine if a particular document is, in fact, a claim.
    • Relationship Extraction: To understand the relationships between entities. For instance, connecting a diagnosis with a specific treatment or procedure.
    • Automated Form Filling: Once relevant information is extracted, populating standardized forms or databases using the extracted data.
    • Error Detection: Using NLP to spot inconsistencies or errors in claims, ensuring higher accuracy.

Oracle

  1. Modeling Server Capacity Data to Predict Outages:
    • ML Techniques:
      • Time Series Analysis & Forecasting: Methods like ARIMA, Prophet, or LSTM (Long Short-Term Memory networks) to predict server capacity based on historical data.
      • Regression Models: For predicting capacity, techniques like Linear Regression or Support Vector Regression might be relevant.
      • Random Forest & Gradient Boosting: Ensemble methods that can predict server outages based on a multitude of factors and historical data.
  2. Predicting Server Health Using LogBERT to Understand Anomalies:
    • NLP Techniques:
      • Transfer Learning: Using a pre-trained model like BERT (in this case, a variant called LogBERT) and fine-tuning it to analyze server logs.
      • Semantic Embeddings: Representing server logs as vectors in a high-dimensional space using embeddings derived from models like BERT.
    • ML Techniques:
      • Anomaly Detection: Techniques such as One-Class SVM, Isolation Forest, or Autoencoders can be employed to detect anomalies in the log embeddings.
      • Clustering: Using unsupervised algorithms like K-Means or DBSCAN to cluster similar logs and identify potential anomalous patterns.
  3. Outlier Detection for Current Latency and Storage Models:
    • ML Techniques:
      • Statistical Methods: Techniques like the Z-Score, Box-Plot, or IQR (Interquartile Range) for basic outlier detection.
      • Isolation Forest: A tree-based method designed specifically for anomaly and outlier detection.
      • Density-Based Spatial Clustering (DBSCAN): Useful for detecting clusters in data and identifying points that do not belong to any cluster as potential outliers.
      • Autoencoders: Neural network-based approach where the network is trained to reproduce the input data, but anomalies produce higher reconstruction errors.

Research

  • I am a research fellow at the University of South Carolina where I collaborate on a few publications I focus mostly on NLP with a little vision and multimodality

    CT2: AI-Generated Text Detection and Introduction of AI Detectability Index (ADI)

Evaluation of Current Techniques for Detecting AI-Generated Text:

  1. Watermarking: A method that subtly tags AI-generated text, using unpredictable word alterations.
    • Shortcoming: These watermarks can be modified or removed, especially if one is aware of the watermarking method.
  2. Perplexity & Burstiness Estimation: Techniques examining how well a model predicts text and the occurrence of word clusters, respectively.
    • Shortcoming: As AI models like GPT-3 become sophisticated, the differences in perplexity and burstiness between AI and human text are less discernible, rendering these metrics less effective.
  3. Negative Log-likelihood Curvature: This observes how small alterations in input affect the model’s output.
    • Shortcoming: It’s not always reliable, especially for sophisticated models like GPT-3, where AI-generated content becomes hard to distinguish from human text.
  4. Stylometric Analysis: A method analyzing linguistic style differences.
    • Shortcoming: Modern AI models like GPT-3 have become adept at mimicking human styles, making it challenging to spot AI-generated content based on stylistic nuances.

Introducing the AI Detectability Index (ADI):

  • What is ADI? It’s a metric combining perplexity (predictability of text) and burstiness (patterns in word choices).

  • Why Perplexity and Burstiness?

    • Perplexity: Measures how predictable a text sequence is. Human writing is believed to have more variability in its predictability.
    • Burstiness: Focuses on the repetitiveness of words or phrases. AI-generated text tends to have more repetitive word patterns.
    • The ADI combines these elements, comparing the metrics against human-written standards, and ranks models based on the detectability of their generated content.

Research Findings:

  • After extensive testing on 15 models, it was evident that conventional methods, including watermarking and stylometric analysis, have limitations, especially when faced with advanced models like GPT-3.
  • While the ADI presents a novel way of leveraging perplexity and burstiness, it’s important to acknowledge that there are still challenges. As AI models evolve, there’s a need for continuous refinement in our detection methods.
Less refined CT2
  • Overview:
  • Research Focus: The paper emphasizes the importance of detecting AI-Generated Text and introduces the AI Detectability Index (ADI).

  • Achievement: This paper received the Best Paper Award for its innovative approach.

  • Background on ADI:
  • Definition: ADI is a composite metric that merges two linguistic measures: perplexity (syntactic) and burstiness (lexical).
  • Empirical Basis: The composition of ADI is founded on empirical observations, and its formulation was influenced by the density function according to Le Cam’s Lemma.

  • Reflection & Future Work: The authors self-reflect, suggesting potential alternative features for ADI, and indicate opportunities for future research to expand and refine the ADI definition.

  • Evaluation of Current AGTD Techniques:
    1. Overview: Various methods have recently been introduced to detect AI-generated text. However, the paper argues that most of these techniques are not robust against state-of-the-art models.
    2. Watermarking: Originally proposed to label AI text by switching high-entropy words, this technique is shown to be vulnerable to strategies like word replacements or paraphrasing.
    3. Perplexity & Burstiness Estimation: These techniques aim to identify statistical differences between AI and human-produced text. However, newer models, such as GPT-3, generate text so similar to humans that these methods become less effective.
    4. Negative Log-likelihood Curvature: This was introduced to identify AI text based on how perturbations influence probability. Yet, empirical evidence from the paper suggests it doesn’t offer a reliable indicator, especially for models like GPT-3.
    5. Stylometric Analysis: This method, aiming to discern linguistic style differences, is found to be constrained when applied to modern advanced models.
  • The ADI is intended to provide a quantitative measure of how detectable an AI system’s generated text is, using current state-of-the-art detection methods. It aims to rank language models based on their detectability spectrum.
  • The ADI equation incorporates two key stylometric features - perplexity and burstiness.
  • Perplexity measures how predictable or expected a sequence of words is. The hypothesis is that human writing will have more variation in perplexity across sentences and passages compared to AI text. So the ADI computes the entropy or variability of perplexity scores across an excerpt of text to quantify this.
  • Burstiness refers to patterns in word choice and vocabulary diversity. The hypothesis is that AI text will display more repetitive bursts of similar words or phrases compared to human writing. So again, the ADI looks at computing entropy of burstiness scores across sentences.
  • The equation combines these two stylometric features, comparing the text excerpt’s perplexity and burstiness entropy to reference levels measured from human-written text. It uses weighting factors to rank the detectability.

  • Through comprehensive experiments on 15 different language models, we found that perplexity and burstiness are not reliable indicators to distinguish AI vs human text, especially for larger models. Other detection methods like watermarking, negative log curvature, and stylometrics also had limitations.

  • So in summary, the ADI equation tries to quantify text detectability using perplexity and burstiness signals, but our experiments revealed fragility in current AI detection techniques, especially for more advanced language models. We hope ADI provides a rubric to guide further research and benchmarking as better methods emerge. Let me know if any part of the equation or results needs more explanation!

Certainly! Let’s delve deeper into each of these techniques:

1. Watermarking:

Concept: Watermarking involves subtly marking or tagging generated text in a manner that identifies it as AI-produced. This is akin to a digital “signature” embedded in the text. The original proposal for this technique was to label AI text by switching high-entropy (unpredictable) words.

Weakness: AI-generated text that’s been watermarked can often be “cleaned” by simply altering, paraphrasing, or replacing certain words. If a malicious actor is aware of the watermarking technique, they can take steps to remove or modify these watermarks, rendering the detection mechanism ineffective.

2. Perplexity & Burstiness Estimation:

Concept:

  • Perplexity is a measure of how well a probability distribution predicts a sample. In the context of text, it gauges how well a language model anticipates the next word in a sequence. High perplexity indicates that the model finds the text unpredictable, while low perplexity means the opposite.
  • Burstiness refers to the phenomenon where certain words or phrases appear in “bursts” or are clustered together more than would be expected by chance.

Weakness: As AI models like GPT-3 have improved, they generate text that’s statistically closer to human writing. This means the distinctions in perplexity and burstiness between AI and human text diminish, making these metrics less effective as discriminators.

3. Negative Log-likelihood Curvature:

Concept: This technique tries to identify AI-generated text based on how small perturbations or changes influence the likelihood or probability of generated sequences. Essentially, it looks at how sensitive the model’s output probability is to small changes in the input.

Weakness: The paper’s empirical evidence indicates that this method doesn’t consistently differentiate between AI and human text, especially when it comes to sophisticated models like GPT-3. As AI models become better, the curvature patterns become less distinguishable from those of human text.

4. Stylometric Analysis:

Concept: Stylometry is the study of linguistic style, and it’s used to attribute authorship to anonymous or disputed documents traditionally. Stylometric analysis seeks to discern differences in writing style between AI and human authors.

Weakness: As AI models have become more advanced, they’ve become better at mimicking human writing styles. This means that the subtle distinctions in style that stylometric methods rely on become less pronounced, making it harder to detect AI-generated content solely based on stylistic attributes.

In summary, while these techniques offer promising approaches to detect AI-generated text, they also come with limitations, especially when dealing with state-of-the-art models like GPT-3. This underscores the importance of continuous research in the field to keep pace with the rapid advancements in AI text generation capabilities.

1) While perplexity and burstiness may not work well in isolation, the authors believe combining them could provide a more robust signal. The ADI formula incorporates both features.

2) The ADI introduces additional factors like using human benchmark comparisons, weighting differences by model detectability, and averaging over many samples. So it enhances perplexity and burstiness in a more comprehensive metric.

3) The authors argue that other detection methods like stylometrics and classifications are ultimately dependent on core features like perplexity and burstiness. So distilling it down to these fundamental elements could offer a universal benchmark.

4) As models evolve, other detection methods may fail, but perplexity and burstiness can still indicate how close models get to mimicking human writing patterns.

  • In essence, the authors are proposing a new way to leverage perplexity and burstiness as part of a more robust and adaptable detection metric in ADI. You raise a very fair point though that they are still utilizing features they demonstrated shortcomings of. More research is needed to validate the effectiveness of ADI as models continue to advance.

Hallucination

  • This paper presents a detailed categorization and analysis of the hallucination phenomenon in large language models (LLMs). The key aspects are:

  1. It defines two orientations of hallucination: Factual Mirage (FM) and Silver Lining (SL). FM is when the LLM distorts a factually correct prompt. SL is when the LLM generates an elaborate narrative for a incorrect prompt.

  2. It further divides FM and SL into intrinsic and extrinsic sub-categories. Intrinsic is minor hallucination supplementary to the topic. Extrinsic is major hallucination deviating from the topic.

  3. Six categories of hallucination are defined: Numeric Nuisance, Acronym Ambiguity, Generated Golem, Virtual Voice, Geographic Erratum, and Time Wrap. These cover common types of factual distortions in LLM outputs.

  4. Three degrees of hallucination are proposed: mild, moderate and alarming - indicating the severity.

  5. To quantify hallucination tendencies, the paper introduces the Hallucination Vulnerability Index (HVI):

\[HVIx = (100 / U*2) * Σu=1U [(N(x) - P(EFM)) * (1 - P(EFM)/N(x) + δ1(x)) + (N(x) - P(ESL)) * (1 - P(ESL)/N(x) + δ2(x))]\]
  • Where:
  • U - Total sentences
  • N(x) - Hallucinated sentences by LLM x
  • P(EFM) - LLM’s tendency for EFM
  • P(ESL) - LLM’s tendency for ESL
  • δ1(x), δ2(x) - Damping factors
  • The HVI provides a 0-100 comparative score of an LLM’s hallucination vulnerability. Higher HVI indicates greater hallucination tendency. The equation accounts for an LLM’s specific extrinsic hallucination biases and overall degree of hallucination.

  1. The HILT dataset of 129K annotated sentences from 15 LLMs is introduced to support analysis.

  2. Mitigation techniques like high entropy word replacement and textual entailment checking are proposed.

In summary, the paper provides a comprehensive framework to characterize and quantify hallucination tendencies in LLMs - defining categories, degrees and introducing the HVI metric. The insights can inform research into mitigation strategies.

Equation

  • If asked to explain the Hallucination Vulnerability Index (HVI) equation in an interview, I would describe it this way:
  • The HVI gives us a score from 0 to 100 that indicates how likely an AI model is to hallucinate or generate false information. A higher score means the model is more prone to hallucinating.
  • The equation works by looking at two main factors - the model’s tendency for extrinsic factual mirage (EFM) and extrinsic silver lining (ESL) hallucinations. EFM is when the model distorts factual information and ESL is when it fabricates details for an incorrect prompt.
  • First, we calculate the number of EFM and ESL hallucinations the model generates out of the total sentences. This gives us the error rates - P(EFM) and P(ESL).
  • Next, we look at how many total hallucinated sentences there are, and compare that to the EFM and ESL rates. The bigger the difference, the more other types of hallucinations there are.
  • We weight those differences by the error rates - so if EFM rate is high, more weight is given to the EFM-related difference.
  • Finally, we factor in the damping parameters which help scale the scores across different models. This gives us the final HVI score.
  • So in essence, the equation accounts for both the specific EFM and ESL tendencies, and the overall degree of hallucination of an AI model. The higher the score, the more likely the model is to hallucinate. It provides a standardized metric to compare different models’ hallucination vulnerabilities.

CONFLATOR: Code Mixing:

Background:

  • Topic: Code-mixing in multilingual societies.
  • Challenge: Traditional Transformers struggle with code-mixed text like “Hinglish” (Hindi-English mix).
  • Key Difficulty: “Switching points” where languages change, making context hard to learn.

The CONFLATOR Model:

  • Core Idea: Emphasize the switching points between languages for better understanding.
  • Innovation: Uses Switching Point Rotatory Matrix (SPRM) to adjust positional encodings when a language switch is detected.
  • Granularity: This technique is applied at two levels - for individual words (unigrams) and word pairs (bigrams).
  • Architecture: Merges both unigram and bigram token representations.

Training & Data:

  • Data Source: Trained on code-mixed Hindi-English tweets and news comments.
  • Training Depth: Base models underwent 100k training steps, while larger models with SPRM underwent 250k steps.

Key Findings:

  • Performance: CONFLATOR surpassed other models in understanding and analyzing mixed-language text.
  • Understanding: The model grasped mixing patterns and recognized switching points better than other methods.
  • Optimal Approach: For sentiment analysis, both unigram and bigram models worked well. However, for translation, the unigram SPRM model was the most effective.

In Summary: CONFLATOR, a novel neural model, introduces a unique approach to decode mixed-language content by focusing on the “switching points” between languages. By integrating this perspective into the positional encoding, it effectively captures the intricacies of mixed languages, making it a potential benchmark in the field of code-mixed language modeling.

Less clear CONFLATOR
  • Certainly, I’ll explain the given passage in simpler terms:
    1. Switching-Point Based Rotary Positional Encoding:
    • The authors introduce a new way to handle positional encoding in neural networks. Positional encoding is a technique used in Transformer architectures (a popular neural network model) to understand the position or order of words in a sentence.
    • The new technique revolves around the idea of “switching points.” Whenever a switch from one language to another occurs in a code-mixed sentence, they change the rotation (or tweak the positional encoding). This helps the model learn when and how languages are mixed within a sentence.
  1. CONFLATOR:
    • This is a new neural network model designed specifically for languages that are code-mixed, like Hinglish.
    • The primary innovation in CONFLATOR is its use of the aforementioned switching-point based rotary positional encoding. Initially, the model looks at each word individually to determine if a switch has occurred. Then, it examines pairs of words (bigrams) to refine its understanding.
  2. Empirical Evidence:
    • The authors claim to have evidence that CONFLATOR successfully learns the patterns of how languages are mixed together in Hinglish. They compare its performance to other models that use different methods to understand the order of words. Their findings suggest that CONFLATOR does a better job at this than other models, as depicted in “Figure 5” (which we don’t have access to in the given text). - In a nutshell, this paper is about introducing a new technique and model for understanding and processing sentences where two languages are mixed together, with a specific focus on the mix of Hindi and English known as “Hinglish.” - Textual Diffusion with Hallucination - Where we’re looking to incorporate factual ground truth during the denoising process to see if that can help mitigate hallucination.

This paper introduces a novel neural language modeling approach called CONFLATOR for code-mixed languages like Hinglish (Hindi-English). Here is a summary explaining the key aspects in an interview-style:

Background:

  • Code-mixing is prevalent in multilingual societies but modeling it is challenging due to data scarcity and unique properties like switching points between languages.

  • Transformers have set benchmarks in language modeling but still struggle on code-mixed text.

  • Switching points are a major bottleneck as the language changes, making it difficult to learn context.

Approach:

  • CONFLATOR hypothesizes that emphasizing switching points is key for code-mixed language modeling.

  • It incorporates positional information of switching points into Rotatory Positional Encodings (RoPE) which capture relative positions.

  • A Switching Point Rotatory Matrix (SPRM) is introduced that changes rotation when a switch point is encountered.

  • This is applied at both unigram and bigram level tokenizations.

  • The architecture merges unigram and bigram attention heads with learnable weights.

Loss Function:

  • Cross-entropy loss is used for the language modeling objective.

  • No specific custom loss function is introduced. The novelty is in the positional encodings.

Training:

  • CONFLATOR is trained from scratch on code-mixed Hindi-English tweets and news comments data.

  • Different configurations with unigram, bigram, SPRM are trained for comparison.

  • Around 100k steps of training is done for base models and 250k for bigger SPRM models.

Key Findings:

  • CONFLATOR outperforms baselines and prior state-of-the-art on intrinsic perplexity and extrinsic sentiment analysis and machine translation tasks.

  • Analysis shows it learns mixing patterns and differentiates switch points better than other positional encodings.

  • Bigram modeling helps for sentiment but not translation. Unigram SPRM achieves best results.

In summary, the key novelty is the switching point based rotatory positional encoding incorporated in a code-mixed neural language model, which shows promise in capturing nuances of mixed languages.

Let me know if you would like me to expand or clarify any part of the explanation! I’m happy to go into more details.

You raise some great points - let me explain the model architecture, inputs/outputs, and objectives in more detail:

Input:

  • The input to CONFLATOR is code-mixed text, such as Hinglish sentences mixing Hindi and English words.

  • For example: “mujhe yeh gaana bahut pasand hai” (I like this song very much).

  • The input text is tokenized into unigrams (individual words) and bigrams (pairs of words).

Architecture:

  • The tokenized input is passed through an embedding layer to get vector representations for each unigram/bigram.

  • These then go through the encoder layers equipped with the proposed Switching Point Rotatory Positional Encoding (SPRoPE).

  • SPRoPE modifies the standard Rotatory PE (RoPE) to change rotation when a language switch point is encountered, helping differentiate switch positions.

  • The unigram and bigram encoder outputs are merged with learnable weights and passed to the decoder.

Objectives:

  • CONFLATOR was trained for language modeling - to predict the next word/token given the previous context. This helps build robust representations.

  • It was also evaluated on downstream tasks:

    • Sentiment analysis of code-mixed text
    • Machine translation from Hinglish to English

Key Outcomes:

  • CONFLATOR outperformed baselines on perplexity (language modeling metric) showing its representations capture code-mixing better.

  • It achieved new state-of-the-art results on the extrinsic tasks, demonstrating the usefulness of emphasizing switch points.

  • The unigram model performed best overall, while bigram helped for sentiment analysis.

In summary, CONFLATOR takes code-mixed text as input, models it using SPRoPE for switch points, and is trained for language modeling and downstream tasks, outperforming previous benchmarks. The key contributions are around representing and incorporating switch points. Let me know if you need any part of the architecture or results clarified!

Deception Detection

  • This paper focuses on detecting lies of omission specifically in textual data from news articles and headlines. The background is that prior work in deception detection has mostly looked at outright lies (lies of commission), while lies of omission have received comparatively little focus.
  • Lies of omission are when someone deliberately leaves out key information, twisting the narrative. For example, a headline could read “New study shows vaccine is dangerous” but omit mentioning that the study was retracted.
  • To explore lies of omission, the authors created a multi-layered dataset called SEPSIS using real news headlines from major outlets as well as existing fake news data. The lies in this dataset are omissions manually introduced based on real-world observations.
  • So in summary, the SEPSIS dataset contains textual news statements and headlines that have different types of deliberate omissions introduced. The paper focuses on detecting these synthetically generated lies of omission, with the goal of gaining insights into how such deception manifests in textual language.
  • The models are not detecting omissions in general text generated by LLMs. Rather, the dataset curation, augmentation and models focus specifically on understanding and detecting lies of omission in the textual news domain. This is an interesting area since such “twisted truth” via omission is hard to pinpoint yet can significantly alter narrative. I hope this helps explain the background and objective behind the paper! Let me know if you need any clarification or have additional questions.
  • This paper presents an interesting study on lies of omission and introduces a novel framework called SEPSIS for detecting deception. Here are the key points about the paper:

Architecture:

  • SEPSIS uses a multi-task learning (MTL) architecture with 4 tasks corresponding to the 4 layers of deception: type of omission, color of lie, intent of lie, and topic of lie.

  • It first obtains word embeddings from a merged language model created by finetuning and dataless merging of multiple T5 models.

  • These embeddings are fed to a transformer encoder to create a latent representation.

  • This representation is passed through 4 multilabel classification heads, one for each task.

Training:

  • Dataset: The SEPSIS dataset contains over 876k data points with multilabel annotations across the 4 deception layers.

  • Augmentation: Paraphrasing and 5W masking were used for data augmentation.

  • Loss functions: A tailored loss function was used for each task head - distribution balanced loss for type, cross entropy for color, focal loss for intent, and dice loss for topic.

  • Model merging helped leverage shared information across tasks and improved performance.

Key Findings:

  • The MTL model achieved an F1 score of 0.87 on SEPSIS, demonstrating effectiveness across all deception layers and categories.

  • Analysis revealed correlations between propaganda techniques and lies of omission, e.g. loaded language correlates with speculation and black lies.

  • Public release of the dataset and code will facilitate more research on deception detection.
  • In summary, the paper presents a novel MTL framework for deception detection using a multilabel dataset, tailored loss functions, data augmentation and model merging techniques. The analysis provides new insights into lies of omission and their connection to propaganda.

Projects mentioned LoRA

Of course! I’ll explain the procedures and intentions behind each of these tasks:

  1. Few-Shot Learning with Pre-trained Language Models:

    • Performed few-shot learning with pre-trained LLM: This means that a small amount of data was used to adapt (“fine-tune”) pre-existing language models (likely designed for broad tasks) to perform a more specific task. The fact that the models are pre-trained indicates that they already have a good grasp of the language due to previous training on large datasets.

    • such as GPT and BERT from HuggingFace’s libraries: The pre-trained models used were GPT and BERT, which are prominent models for understanding language context. These models were sourced from HuggingFace, a leading provider of state-of-the-art language models.

    • Experimented with more sophisticated fine-tuning methods such as LoRA: After starting with basic fine-tuning, more advanced methods were employed. LoRA (Localized Re-adaptation) is one such method that provides a sophisticated way to adapt a pre-trained model to a new task using a limited amount of data.

    • Used PyTorch framework: All the experiments and model training were done using PyTorch, which is a popular deep learning framework. This gives information about the tools and libraries that might have been employed during the procedure.

  2. Multitask Training for Recommender Systems:

    • Implemented a multi-task movie recommender system: A recommender system was developed that can handle multiple tasks simultaneously. In this context, it might mean recommending various types of content or handling different aspects of recommendations concurrently.

    • based on the classic Matrix Factorization and Neural Collaborative Filtering algorithms: The foundational techniques used for this recommender system are:

      • Matrix Factorization: It’s a technique where user-item interactions are represented as a matrix, and then this matrix is decomposed into multiple matrices representing latent factors. This is a traditional technique often used in recommendation systems.
      • Neural Collaborative Filtering: This is a more modern technique that uses neural networks to predict user-item interactions, thus providing recommendations.

In summary, the first task involved adapting large, general-purpose language models for specific tasks using a small amount of data, while the second task was about building a multi-task recommendation system using traditional and neural techniques.

Technical Breadth

  • I love collaboration and thinking outside the box. Amazon devices, the primary goal was for users to shop.
  • So what I’ve been trying to do is, find correlation between retail items and songs, both for the website and Alexa as well.
  • Item to item recommendations are bread and butter of Amazon

People management

  • I like to lead with empathy
  • Mentorship: make sure everyone has a mentor, helping them find one if not
  • people growth
  • upward influencing: offerring solutions, understanding the perspectives and goals

Agility

Motivation

  • The research coming out of Meta is an inspiration in itself, Meta is a trailblaizer in so many domains:
  • Text to speech: Voicebox where its able to do speech generation tasks it was not necessarily trained on
  • Pure NLP: No language left behind project with translations between 200 languages and including the work with low-resource languages is something I really connect with.
  • Recommender system: embedding based retrieval and so many more
  • And I imagine Smart glasses org to be a culmination of all of this research and so, to be given the opportunity to work there would be a true joy.

Questions for the manager

  • Team structure, I assume since it’s lenses, theres collaboration with a vision team. Are there other modalities at play?
  • Hallucination is the biggest problem with LLMs
  • Smart Glasses (SG) Language AI

  • We focus on Conversational AI, SG Input AI, Knowledge-enriched Discovery AI, Privacy ML and AI Efficiency. Our system powers critical SG launches thrusted by the Meta leadership. We have strong scientists and engineers, solving challenging AI problems with both Cloud based large models and On-Device ML models. Join us if you are passionate about AI-centric next-gen computing platforms and pushing advanced AI at production scale!

  • Our team: Smart input team’s mission is to enhance input and messaging experience on these smart glasses. Imagine being able to receive your whatsapp messages, and being able to get more context (summary) and respond in a natural way just like how you would have a conversation with a human, all while wearing your glasses and not taking your attention off the things you are doing (like biking, cooking, walking with your grocery bags).
  • Our tech: We want to bring ChatGPT capabilities on-device. We build the capabilities similar to what ChatGPT can do for messaging but with a model that is much smaller to be able to fit on the glasses. This is a niche tech space with big opportunities to innovate on LLMs, on-device ML, privacy ML such as Federated learning, on-device personalization. This team aims to ship these cool technologies to drive end user value.

  • While the rest of the world is going after making LLMs work on the servers, we are taking a bigger challenge to make LLMs work on-device.

  • In a more constrained use case, such as using natural language processing (NLP) to interpret voice commands for Amazon Music, the problem of hallucination might be less prominent. In this case, the system is less likely to “make things up” because it’s not primarily generating content but rather interpreting and executing commands based on user input. If the NLP system doesn’t understand a command, it’s more likely to ask for clarification or fall back to a default action rather than inventing an inappropriate response.
  • However, hallucination could still be an issue if the system uses language models to generate responses or explanations. For example, if you ask Alexa for information about a particular artist or song, and it uses a language model to generate the response, it could potentially hallucinate details that aren’t true.
  • In any case, whether hallucination is a problem depends on the specific application and how the system is designed and used. It’s an active area of research in AI to find ways to mitigate this issue, especially as language models are being used in more diverse and impactful applications. Techniques like fine-tuning the model on specific tasks or data, utilizing structured data sources to ground the responses, or using model validation to check the outputs could help to limit hallucination.

HOld

RAG

  • Implementing RAG in an AI-driven application entails the subsequent procedures:
  1. The user submits a query.
  2. The application scans for pertinent documents that could potentially address the query from a document index, which usually comprises proprietary information.
  3. The application crafts an LLM prompt by merging the user’s query, the identified documents, and directives for the LLM to respond using the given documents.
  4. This constructed prompt is then dispatched to the LLM.
  5. Based on the provided context, the LLM produces a response to the user’s query, which serves as the system’s final output.

Onsite

  • Round 1 in-domain design: Actually just talk about the research you have done
  • Round 2 AI coding: Leetcode 1570. Find a local minimum and return index
  • Round 3 AI research design: Apply the model you made in previous research to another scene
  • Round 4 AI research design: explain your recent paper or project
  • Round 5 BQ:
  • Describe a project you are most proud of.
  • Describe a time when you have to make a decision with insufficient information or conflict with others.

  • Describe a time when you insisted on doing something and it turned out to be wrong.

  • a total of four rounds, 2coding+ML design+BQ
  • first round ml design, Korean girl, design facebook event predictor. I asked how to collect data, how to do feature engineering, what model to use, how to evaluate,
  • how to improve, and some basic ML questions.

  • In the second round of coding, I met an unreliable interviewer. The interviewer was almost 20 minutes late.

  • I just interviewed for the Meta Reality Labs 2023 summer research intern last week, and I’m still waiting for the results. I’d like to share my feelings and experiences here. I have interviewed the original poster a few years ago, so the following is a summary of several interviews.
  • Summer internship for Meta Reality Labs research position with CV emphasis. The Meta Careers website will list the positions of different teams in great detail. Just choose the one that matches your background and apply. You can apply for multiple positions at the same time. Research positions are sometimes divided into research scientists and research engineers, but personally I feel there is not much difference. Most research internships only recruit PhDs, but there are also MS/PhDs, which will be written in the title.
  • After submitting the application, if there is a match, HR will contact you via email to ask some basic questions and then schedule an interview. Generally, there are two interviews, one is the research interview and the other is technical (coding), both of which are 45 minutes. If you are invited to interview for multiple positions you applied for, you will only have to interview once for coding, but you will probably have separate interviews with different teams for research. Results are generally given 1-2 weeks after the interview.
    1. Research interview: Interview with an on-the-job research scientist. The interviewer may be the person who will guide you in the future. The interviewer will first introduce what his team does roughly, and then ask you to introduce your research. Then he will ask about some knowledge related to this position or the problems he wants to solve for this project, and ask you what you think. Five minutes will be left at the end for you to ask him questions. .
    2. Technical (coding): On-site programming on the website they provide, usually two questions. The website can only be coded and cannot be run, but the interviewer may ask you to simulate the operation of a set of data on the whiteboard of the website. Then ask about the time and space complexity of the algorithm you wrote and whether it can be optimized, and observe whether you can handle the corner case properly. Unlike SDE, the focus of research intern is to solve research problems, so the coding ability may not be too high. I have seen two types of questions, one is Leetcode style, and the other is AI-related coding. We PhD students may rarely do LeetCode, but we still need to prepare a little before the interview. I got a Breadth-First Search question in the first interview. Although it is not very difficult, because I learned it during my undergraduate studies, it was so long ago that I would not be able to write it if I was not prepared. The other is AI-related coding, such as writing some data processing, augmentation, calculating some metrics, etc.
  • Personally, I felt that my interviews were pretty good (everyone I interviewed in the past few years ended up with an offer), and the atmosphere was very good. It didn’t feel like they were interviewing me, but more like chatting about research. So let me briefly talk about my feelings and what I think I did better.
    1. The interviewer’s first name and last initial will be informed in the email. Combined with the team information, you can usually Google this person, so you can prepare in advance to see what he does, and then you can make targeted adjustments when introducing yourself for research. For example, I know that he should be very familiar with a certain background, so there is no need to introduce it in detail, or I can predict which work he has done before that he will be interested in. At the same time, you can roughly estimate the interviewer’s level (his title is included in the email) to have a rough prediction of the interview style: a high-level interviewer will care about higher-level questions and the practical application prospects of your research, while The interviewer with a lower level is likely to be the person who will directly interview you in the future, so he will ask more specific and project-related questions. I have encountered both types of interviewers above, and their styles are quite different. . Waral di, 2. At the beginning, you will be asked to introduce your research, mainly to know what you generally do, without going into too much detail. It is recommended to prepare a 5-min slides to roughly introduce your research direction and give a broad framework context. It is best to string together your previous work into a complete story, but do not introduce specific work. After the introduction, you can ask him to give more details if he is interested in a specific job. advertise
    2. Be familiar with where the things you have done are (on the computer or online), especially the various pictures. If you are asked a relevant question and need to be able to quickly pull up the picture to share the screen, pointing to the picture to answer the question is much more effective than just talking. And many questions are asked around resumes, so you
    3. There will be a few minutes left for you to ask questions at the end of the interview, so prepare some questions to avoid not thinking of good questions on the spot. Generally, you ask about project-related questions, such as expected outcome (publish a paper? Implement a certain system? There is a big gap between groups in this area), for example, ask what background the interviewer thinks you still lack (if you actually don’t lack it) You can add that background to him at this time; otherwise, thank him and say that he can add it as soon as possible), such as location (if the team has many offices), or think of other valuable issues. Don’t ask about interns’ benefits and benefits. You should ask HR about such questions. (Personally, I feel that Meta’s intern benefits are very good, so don’t worry too much)
  • When sharing experiences in the forum, I also want to find friends who share the same direction and share the same goals with me!
  • The full name of the position is Research Engineer Intern, CV, AI and Machine Perception (PhD/Master) - Redmond, WA. It actually does SLAM related things. Under FRL, I was very excited when I saw the recruitment of master. FRL should be There are many dream places in this field, and many great gods are in them. Being rejected is indeed because my skills are inferior to others, and my interview performance is very poor…
  • 11.1 After knowing this position, I asked my friends to recommend again (I was rejected once when I applied for a general position before with my resume)
  • 11.29 VO two rounds, the first round Project interview (similar to team match), the second round is coding, this position is like this, the
  • first round of direct VO : I was asked about the details of lidar calibration at my previous company, and without saying much, I started to directly ask about domain knowledge. 1.
  • Vector operations, Eigen, are very basic, just dot product and cross product
    1. Homography, I haven’t done vSLAM for a long time. I asked if I could talk about epipolar constraints. He asked me to calculate the formula on the spot. The formula was also listed in the drawing. After it came out, I finally forgot how to draw it in the form of essential matrix.
    2. Asked me if I knew about Linear Programming. At that time, the algorithm class had not yet reached this part, and I didn’t realize that LP is linear programming. Let’s see if I don’t change it again. Cheng asked me about the solution of linear equations, epic ancient knowledge, but he bit the bullet and said nothing.
    3. Image rotation, I mentioned bilinear interpolation, but I answered this question well.
  • Second round :1 . The first question is to find the median of a given array. There should be this question in the tag. I forgot the question number. I directly used min_heap to do            2. followup gives you a two-dimensional matrix, and then gives you the kernel size k, and then asks you do something like

  • technology stack, some internally focus on lidar, some focus on vSLAM, and some focus on 3D reconstruction. The details of each company are very different, and it is quite difficult to prepare. Review some very basic and mathematical things again. This Meta interview was the most difficult one I encountered. It involved pushing formulas on the spot and asking questions about ancient linear algebra. My past projects basically didn’t ask any questions. The coding interview was a confusing followup. I was not as good as others, but I accepted it even if I failed. I only have an intern offer in Tucson, so it seems like the only option is to go to Tucson. I hope I can go to a better place full-time next year, so that I can live up to my trip to the United States.

  • research round:
  • each talks about his own project, I talk about my own research direction, then he introduces the idea that the team wants to do, and then I make some assumptions based on this idea. .
  • When talking about the project, he will talk about the end-to-end process. From the beginning of the project, sometimes he wants to hear the technical details, and sometimes he wants to hear what [I] did in the project. Even if they are similar, everyone is analyzing the needs. Do literature reviews and other routines. . .
  • (If you think about it carefully, this might be closest to bq?
  • (Or is it like taking a speaking test?
  • Coding wheel:
  • sum of the cropped_image
  • image = [[0 1 0 1 0]
  • leftTop = [1 1]
  • rightBottom = [2 3]
  • At this moment, cropped_image = [[0 0 0],

dig Meta

    1. Storefront: Two questions, original title of Li Kou Er Umbrella Liu; variant of Li Kou Er San (no constraint on subarray with size at least two), Chinese guy, amiable; 2. Onsite: Round1(coding)
  • three Questions, the original question of Likou San Umbrella Wine; the original question of Likou Yao Umbrella; the original question of Likou Er Erqi (it is a bonus, there is no space o(1) requirement, but I still gave one, and the interviewer felt that it was quite good Surprise), white guy, approachable;
  • Round 2 (coding): two questions, Likou Liuwu variant (no constraints are given at all, only a few examples are given, so you need to keep clarifying with the other person, it seems that this is what he wants to see in the question) part of , no sign char); Likou Liuqi Ling (gave a solution of o(n^2), and then the other party chased for a better solution, but did not give it, so the other party gave a solution of o(nlogn), which seemed to be OK There are o(n)), black brother, gentle and elegant;
  • Round3(ML): MLsystem design, the case is about the Facebook Event recommendation system, the target is to determine whether the user will participate in these events, the difficulty is to determine whether they are physically present, there is nothing special in other places), the Indian guy, the attitude is okay; Round4 (BQ)
  • : constructive feedback, collaboration with different
  • , but I don’t feel that the overall interview process reflects personal professional abilities.
    1. Timeline:
  • I was hooked up by HR at the August meeting, but my student was postponed to November because I didn’t pass the exam at all; the
  • store in mid-November will be updated one day later;

  • the onsite one week before Christmas will be completed in three days. Then because of the holiday season, I waited until the evening of January 4 to get an appointment, and the next day I called for an oral offer.

  • The first round of chatting with the person in charge of the group was mainly to talk about the research I did and the papers I published, as well as the problems I would like to solve if I get an internship. In fact, the research direction is not particularly suitable, but the chat was quite pleasant. I heard there might be a return in the group.
  • The second round of AI coding feels completely different from the common questions in the field…
  • The first question is to give a matrix and determine whether the values ​​of each diagonal (upper left to lower right) are the same. For example, [[0,1,2],[3,0,1],[4,3,0]] is satisfied. After I finish writing a method, I analyze it
  • Turn it upside down (the title is byte swap). I wrote a simple algorithm, but I don’t know if I ran out of time and didn’t let me optimize it further.
  • Generally speaking, coding is a bit difficult, but it’s really great without asking BQ.

AI coding and onsite:

  • Write K means, write knn, implement a conv 2d, write code to calculate precision recall
  • I was tested to achieve the target detection iou, but there are similar questions in lc, and I was also tested.
  • AUC , RUc curve. Cross entropy loss. Tensor manipulation. My guess
  • Round 1 in-domain design: Actually just talk about the research you have done
  • Round 2 AI coding: Leetcode 1570. Find a local minimum and return index
  • Round 3 AI research design: Apply the model you made in previous research to another scene
  • Round 4 AI research design: explain your recent paper or project
  • Round 5 BQ:
  • Describe a project you are most proud of.
  • Describe a time when you have to make a decision with insufficient information or conflict with others.
  • Describe a time when you insisted on doing something and it turned out to be wrong.
  • Negative feedbacks from your managers or peers.
  • First give a presentation to introduce your research, so that people in the group understand what direction you are working on, a 45-minute presentation and a 15-minute QA.
  • Then there are two rounds of research interviews. Basically, people from FAIR come to interview. They mainly focus on their own research. They will ask some questions about the current SOTA insights and the development direction of the field. Generally, the research aspect is the easiest and most interesting part. Each round is 45 minutes.
  • followed bysystem designThere is basically no need to prepare for the interview. The questions are generally about all aspects involved in putting some tasks on the meta platform for users to use, such as Youtube video recommendation, and AR glass for smart purchases in stores, etc. Although the topic It will change, but after all, they are all CV-related tasks. As long as you are familiar with the conventional methods of these tasks, there is basically no big problem.
  • Next is the coding interview. Fortunately, the coding side of researchers seems to have been reformed. In principle, it does not answer Leetcode questions, but the research coding side. What I encountered was to write an
  • online code to calculate the mean and variance, and a calculation about average precision.
  • AUC questions. The complexity of calculating AP’s requirements is O(n), so I didn’t finish it and finally just talked about the idea. But different aspects
  • Almost medium is the classic sparse vector dot product question that Meta often takes.
  • Provide a dp. The two rounds I went through were the same. I asked about research at the beginning, which was very detailed, and various follow ups. In the last 10 minutes, coding was not a sharp question, but it was about implementing a variant of a certain operation in the AI ​​algorithm. Pure implementation, not API adjustment
  • LZ started overseas investment around January. I received the interview invitation at the end of February and the interview at the beginning of March. There are two rounds of interviews, one is Team Match and the other is AI conding.
  • The first round of chatting with the person in charge of the group was mainly to talk about the research I did and the papers I published, as well as the problems I would like to solve if I get an internship. In fact, the research direction is not particularly suitable, but the chat was quite pleasant. I heard there might be a return in the group.
  • The second round of AI coding feels completely different from the common questions in the field… .google и
  • The first question is to give a matrix and determine whether the values of each diagonal (upper left to lower right) are the same. For example, [[0,1,2],[3,0,1],[4,3,0]] is satisfied. After I wrote a method and analyzed it
  • Turn it upside down (the title is byte swap). I wrote a simple algorithm, but I don’t know if I ran out of time and didn’t let me optimize it . 1point3acres Generally speaking, coding is a bit difficult, but it’s really great without asking BQ.
  • I just interviewed for the Meta Reality Labs 2023 summer research intern last week, and I’m still waiting for the results. I’d like to share my feelings and experiences here. I have interviewed the original poster a few years ago, so the following is a summary of several interviews.
  • [Interview Position] Summer internship for Meta Reality Labs research position with CV focus. The Meta Careers website will list the positions of different teams in great detail. Just choose the one that matches your background and apply. You can apply for multiple positions at the same time. Research positions are sometimes divided into research scientists and research engineers, but personally I feel there is not much difference. Most research internships only recruit PhDs, but there are also MS/PhDs, which will be written in the title. . Waral dи,
  • [Interview Process] After submitting the application, if there is a match, HR will contact you via email to ask some basic questions and then schedule an interview. Generally, there are two interviews, one is the research interview and the other is technical (coding), both of which are 45 minutes. If you are invited to interview for multiple positions you applied for, you will only have to interview once for coding, but you will probably have separate interviews with different teams for research. Results are generally given 1-2 weeks after the interview.
    1. Research interview: Interview with an on-the-job research scientist. The interviewer may be the person who will guide you in the future. The interviewer will first introduce what his team does roughly, and then ask you to introduce your research. Then he will ask about some knowledge related to this position or the problems he wants to solve for this project, and ask you what you think. Five minutes will be left at the end for you to ask him questions.
    2. Technical (coding): Live programming on the website they provide, usually two questions. The website can only be coded and cannot be run, but the interviewer may ask you to simulate the operation of a set of data on the whiteboard of the website. Then ask about the time and space complexity of the algorithm you wrote and whether it can be optimized, and observe whether you can handle the corner case properly. Unlike SDE, the focus of research intern is to solve research problems, so the coding ability may not be too high. I have seen two types of questions, one is Leetcode style, and the other is AI-related coding. We PhD students may rarely do LeetCode, but we still need to prepare a little before the interview. I got a Breadth-First Search question in the first interview. Although it is not very difficult, because I learned it during my undergraduate studies, it was so long ago that I would not be able to write it if I was not prepared. The other is AI-related coding, such as writing some data processing, augmentation, calculating some metrics, etc.
  • [Interview experience] Personally, I felt that my interviews were pretty good (everyone I interviewed in the past few years ended up with an offer), and the atmosphere was very good. It didn’t feel like they were interviewing me, but more like chatting about research. So let me briefly talk about my feelings and what I think I did better. . 1point3acres
    1. The interviewer’s first name and last initial will be informed in the email. Combined with the team information, you can usually Google this person, so you can prepare in advance to see what he does, and then you can make targeted adjustments when introducing yourself for research. . For example, I know that he should be very familiar with a certain background, so there is no need to introduce it in detail, or I can predict which work he has done before that he will be interested in. At the same time, you can roughly estimate the interviewer’s level (his title is included in the email) to have a rough prediction of the interview style: a high-level interviewer will care about higher-level questions and the practical application prospects of your research, while The interviewer with a lower level is likely to be the person who will directly interview you in the future, so he will ask more specific and project-related questions. I have encountered both types of interviewers above, and their styles are quite different.
    2. At the beginning, you will be asked to introduce your research, mainly because I want to know what you generally do, without going into too much detail. It is recommended to prepare a 5-min slides to roughly introduce your research direction and give a broad framework context. It is best to string together your previous work into a complete story, but do not introduce specific work. After the introduction, you can ask him to give more details if he is interested in a specific job. . 1point3acres.com 3. Be familiar with where the things you have done are (on the computer or online), especially the various pictures. If you are asked a relevant question and need to be able to quickly pull up the picture to share the screen, pointing to the picture to answer the question is much more effective than just talking. And many questions are asked around the resume, so you should be familiar with the things on your resume and be able to give details at any time. advertise
    3. Don’t get bogged down in details (unless the interviewer asks). One time meeting
  • The officers are all very friendly, so don’t be nervous. When chatting, give more feedback with facial expressions and body language. For example, when you listen to him, you can nod at the right time to show that you understand. When you explain a problem to him, glance at his expression in the small window to see if he is following the topic he is talking about. Add your own ideas in a timely manner and communicate more to avoid one party’s continuous output. Once the interview becomes a chat atmosphere, you won’t be nervous.
    1. There will be a few minutes left for you to ask questions at the end of the interview, so prepare some questions to avoid not thinking of good questions on the spot. Generally, you ask about project-related questions, such as expected outcome (publish a paper? Implement a certain system? There is a big gap between groups in this area), for example, ask what background the interviewer thinks you still lack (if you actually don’t lack it) You can add that background to him at this time; otherwise, thank him and say that he can add it as soon as possible), such as location (if the team has many offices), or think of other valuable issues. Don’t ask about interns’ benefits and benefits. You should ask HR about such questions. (Personally, I feel that Meta’s intern benefits are very good, so don’t worry too much)
  • Finally, please give me some points for newbies, otherwise I won’t be able to read many posts with points requirements.
  • matrix size is nm. slide window size is given pq. You need to find the median of all possible sliding windows. Starting from the upper left corner, the sliding window moves until it reaches the lower right corner.
  • 5 rounds of interviews: 2 rounds of coding, 1 round of ML design, 1 roundsystem design, 1 round of BQ.
  • Coding is a high-frequency question. There is one question that impressed me deeply. Given an mxn matrix, find the median in the sliding window. I have seen similar problems before, and it seems that there is no particularly good solution. I can only solve it violently, sort the numbers in each sliding window and find the middle value. Later, the interviewer reminded me that the values ​​in the matrix are integers and their range is very small, so you can count sort and find the middle value. .
  • System design is to build a competition website similar to leetcode. When the competition starts, contestants can see the questions on the website, then write code and submit answers. The website will test whether the code passes and rank. It seems that I have seen similar questions with the manager. It is not particularly complicated. Please note that the volume may be a bit large at the beginning and end of the game, and the evaluate code will be slow. Use a queue to queue up. I am not a CS major, and I originally wanted to change this rotation to ML design, but the recruiter said that the policy has changed and I will not change it. . As a result, I could only find information on the Internet and learn blindly. But it seems that this round has passed because there is no need to add system design.
  • Here comes the point, ML design asks how to recommend posts that are not in Moments to the user’s newsfeed. I just recently read a tech blog on Instagram, which talked about how to do user embedding. I was secretly happy, thinking that I would just follow the instructions, eh. . Let me first say that you can use a simple method, popularity based, content b
  • Driven decision, both can be determined by testing the data]. In the diversify step, I only talked about adding a penalty. I recently read some papers showing some fancy methods, but they are also a bit niche. The interviewer probably won’t downgrade me because of this. That’s all I can think of, I’ll add more if I think of anything else.
  • After the interview, the host felt that it was okay and thought there was hope, but he was waiting for an additional interview. It’s not the worst outcome, it’s just that I was too tired from preparing for the interview and I couldn’t stand my face. And I don’t know how to prepare for the senior bar. A little confused. I sincerely ask for your advice.
  • HR reached out at the end of March, and I had an interview in the first week of April. There were two rounds of interviews, one round of AI conding, one round of AI Research Design,
  • the first round of sparse matrix, and then some optimization questions. Sure enough, the coding in the RS interview was very poor. Later, in order to buy time, I let KNN hold up my hands
  • for the second round to talk about my research. The interviewer before was working in NLP and I was in the same direction, but I temporarily changed to one working in CV and wear‍‌‌
  • ML design: Design a classifier for bad ads BQ: A time you had conflict with others, most challenging project, peer you felt difficult to work with, a time you provided constructive feedback. NL
  • I have gone through four rounds of interviews, and the remaining coding round requires rescheduling. Please bless and pass smoothly. 1 privy, jiu Yiyi 2 design a personalized location recommendation system 3 behavior: conflict with other, most challenging, received sharp feedback, most ambiguous project, 4 tree based model, neural netvariance, overfitting, regularizations, evaluation metrics,

Yashar Topics

  1. Meta Conversational AI Agents Research & Products, Large Language Models:
    • Research in this area focuses on developing AI agents that can engage in meta-conversations, which means they understand and manage the conversation itself. This involves recognizing the context, handling interruptions, changing subjects gracefully, and exhibiting awareness of conversational dynamics.
    • Products derived from this research might include customer service bots that can better manage complex interactions, virtual assistants that can handle multiple threads of conversation, or AI mediators that facilitate group discussions online.
  2. AI Multimodal Generation for Metaverse and VR products:
    • This involves creating AI systems capable of generating content not just in text but in 2D and 3D spaces, such as creating virtual objects, environments, or characters in the Metaverse and VR platforms.
    • Products here include VR game elements autonomously generated by AI to enhance user experience, or AI tools that assist designers in creating complex 3D models for the Metaverse.
  3. AI Multimodal Understanding and Recommendation for Metaverse and VR Products:
    • Such systems are designed to understand inputs from multiple sources – visual, auditory, and potentially haptic (touch) – to provide contextual recommendations. In the Metaverse, this could mean suggesting events, interactions, or items based on user behavior and preferences.
    • Products utilizing this research might include virtual environments that adapt in real-time to user actions, or recommendation systems that suggest relevant VR content or products.
  4. AI Multimodal Solutions for Making the Metaverse & VR Products Safe:
    • This field aims to create AI that can interpret and moderate content across various modalities to ensure user safety and integrity within VR environments. It includes identifying and acting upon harmful behavior or content.
    • Products involve AI moderation tools that can automatically detect and mitigate toxic behavior, ensure compliance with community guidelines, and protect user privacy and security within VR spaces.
  5. Neural Retrieval and Question Answering from Research to Products:
    • Neural retrieval involves using neural network-based methods to improve the search and retrieval of information. Combined with question-answering capabilities, this research can create systems that understand and provide precise answers to user inquiries.
    • In products, this might look like more intuitive and accurate search engines within communities or groups, voice-activated devices that better understand natural language queries, or smarter virtual assistants.
  6. Conversation Understanding and Summarization from Research to Products:
    • This research focuses on creating AI that can not only understand conversation nuances but also summarize key points and actions. It’s particularly valuable in workplace settings where capturing meeting notes and actions is important.
    • Products developed from this could include meeting assistant tools that provide summaries and action items post-discussion, or AR devices that can provide real-time annotations during live conversations.
  7. Language Technologies for XR:
    • In XR (extended reality, which encompasses AR, VR, and everything in between), language technologies are crucial for user interaction. The aim is to improve the quality and scalability of language understanding and generation, making it more efficient and responsible.
    • This includes AI-driven language services that work across all languages, understanding accents, dialects, and context, as well as generating responses and content that are contextually relevant and culturally appropriate. It spans tools for automatic translation, speech recognition, and content creation that can operate within the immersive environments of XR.

References