What is the difference between encoder and decoder architectures of neural networks? Describe the transformer architecture in detail.

Building a model in a notebook is different from deploying it to production. Interviewers look for experience with versioning, monitoring, rollback strategies, and handling edge cases at scale.

4. Cross-Functional Collaboration

ML projects typically require working with data engineers, product managers, and software engineers. How did you communicate technical concepts to non-technical stakeholders? How did you gather requirements?

• Brief project context and business problem
• Your role and team structure
• Timeline and project scope

Minutes 2-5: Problem Formulation

• How you defined the ML problem
• Success metrics and constraints
• Dataset overview and data collection challenges

Minutes 5-10: Technical Deep Dive

• Model architecture selection and rationale
• Training approach and experimentation process
• Feature engineering decisions
• Key technical challenges you encountered

Minutes 10-13: Production Deployment

• Serving infrastructure and architecture
• Performance requirements (latency, throughput)
• Integration with existing systems
• Monitoring and alerting setup

Minutes 13-15: Results and Learnings

• Measurable business impact
• Model performance metrics
• Unexpected challenges and how you addressed them
• What you'd do differently next time

Sample Walkthrough Introduction: "I led the development of a content recommendation system for our streaming platform that personalized video suggestions for 5 million daily active users. I was the ML engineer on a team of 6, working alongside 2 data engineers, 2 backend engineers, and a product manager over a 4-month period."

Problem Formulation: "The business goal was to increase user engagement, specifically time spent on platform and session length. We framed this as a ranking problem: given a user's viewing history and context, predict which videos they're most likely to watch next. Our key metrics were click-through rate and watch-through rate. The main constraint was inference latency—we needed predictions in under 100ms to avoid impacting page load times."

Technical Deep Dive: "We experimented with three approaches: a collaborative filtering baseline, a two-tower neural network, and a transformer-based model. The transformer showed 15% better offline accuracy but had 3x higher inference latency. We ultimately chose the two-tower architecture because it offered a good balance—12% better than baseline with acceptable latency. For features, we used viewing history embeddings, time-of-day signals, and content metadata. The hardest challenge was handling the cold-start problem for new users. We solved this by incorporating content-based features and having a fallback to popularity-based recommendations when we had fewer than 5 interactions."

Production Deployment: "We deployed using a two-stage architecture. The candidate generation stage used approximate nearest neighbor search to retrieve 1000 candidates from our 100k video catalog in under 20ms. The ranking stage used our trained model to score these candidates. We served the model using TensorFlow Serving on Kubernetes with auto-scaling based on request volume. We implemented feature logging to capture what features were used for each prediction, which was crucial for debugging. For monitoring, we tracked both technical metrics like p99 latency and business metrics like CTR and watch time, with alerts if either degraded beyond thresholds."

Results and Learnings: "After a 2-week A/B test with 5% of users, we saw 8% higher session duration and 12% higher CTR compared to the rule-based system we were replacing. The model maintained stable performance for 3 months before we needed to retrain. One surprise was that our model performed worse on weekends—users had different viewing patterns, and we hadn't captured enough day-of-week signal. We fixed this in the next iteration. If I were to do it again, I'd invest more time upfront in the data quality pipeline. We spent about 30% of the project fixing data consistency issues that could have been prevented with better validation earlier."

Results and Learnings: "After a 2-week A/B test with 5% of users, we saw 8% higher session duration and 12% higher CTR compared to the rule-based system we were replacing. The model maintained stable performance for 3 months before we needed to retrain. One surprise was that our model performed worse on weekends—users had different viewing patterns, and we hadn't captured enough day-of-week signal. We fixed this in the next iteration. If I were to do it again, I'd invest more time upfront in the data quality pipeline. We spent about 30% of the project fixing data consistency issues that could have been prevented with better validation earlier."