Welcoming questions
- Can you tell us about yourself and how you moved into machine learning engineering?
- Which ML system you have shipped are you most proud of, and what impact did it have?
Role-specific / technical competencies
- Walk us through the lifecycle of a model you took to production, from problem framing to monitoring.
- How do you evaluate a model before deployment, and how do you guard against data leakage?
- How do you detect and respond to model drift or performance degradation in production?
- Describe your experience with feature pipelines. How do you keep training and serving data consistent?
- When would you choose a simple baseline over a deep learning approach? Give a real example from your work.
Behavioural & culture fit
- Tell us about a time a model you believed in failed to deliver business value. What did you do?
- Describe a disagreement with a product manager or scientist about model requirements. How was it resolved?
- How do you explain model limitations and risks to non-technical stakeholders?
Problem-solving / case
- Our recommendation model performs well offline but shows no lift in the A/B test. How would you investigate?
- We want to add an LLM-powered feature to the product. How would you design the evaluation before launch?
- Inference costs for a production model are growing faster than usage. What options would you explore to bring them down?
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