Amazon Machine Learning Engineer Mock Interview

Describe a time you redesigned an ML system to improve latency or cost (e.g., reducing inference time, compressing models, improving batching). What trade-offs did you make, how did you measure success, and what would you do differently today?

Amazon values ownership—tell me about a time you took full responsibility for a model from problem framing through deployment and monitoring. How did you handle ambiguous requirements, stakeholder alignment, and production issues when the model behaved differently than expected?

A recommendation or ranking model in production starts degrading after a feature change upstream. Walk me through how you would diagnose the root cause using logging/metrics, offline vs. online comparisons, data drift detection, and model debugging. What specific signals or tests would you run first?

How would you approach training and evaluating a personalization model for a large-scale marketplace with extreme sparsity and delayed feedback (e.g., purchases occurring long after impressions)? Include how you’d define labels, choose loss functions, and validate improvements in a way that would satisfy an Amazon-style experimentation culture.

Imagine you’re tasked with improving CTR for Sponsored Products, but you’re concerned about harming user experience or creating unintended bias across sellers. Share how you would incorporate fairness/safety constraints or guardrails into the modeling and how you’d set thresholds for rollout and monitoring.