"Top 10 Questions to Ask When Hiring Machine Learning Engineers"

This checklist gives non-technical and technical hiring managers practical questions to evaluate machine learning engineers.

Core areas to cover

• Problem solving and system design
• Data handling and feature engineering
• Modeling choices and tradeoffs
• Productionization and monitoring
• Team fit and communication

Top 10 questions (with what to listen for)

1. Describe a model you deployed and the impact it had. (Look for metrics, constraints, and tradeoffs.)
2. How do you handle missing or biased data? (Practical strategies expected.)
3. Explain a time you optimized model latency or inference cost. (Production exposure.)
4. How do you evaluate model drift? (Monitoring mindset.)
5. Walk through a feature engineering decision you made. (Signal extraction.)
6. Which frameworks do you prefer and why? (Depth of tooling knowledge.)
7. How do you collaborate with product and engineering teams? (Communication.)
8. Describe a failure and what you learned. (Ownership and learning.)
9. Explain how you’d design an A/B test for a model. (Experimentation skills.)
10. What ethical considerations did you face in a past project? (Safety awareness.)

Use these questions as conversation starters, and combine them with a short practical exercise for high-confidence hires.

CTA: Want a tailored interview rubric for your role? Reach out for a custom checklist.