ML Engineer Mock Interview: 40 Production ML Interview Questions

ML engineering sits at the intersection of

machine learning software engineering data systems infrastructure That makes interview preparation particularly broad. Machine learning fundamentals

1. How do you choose a model?

2. What causes overfitting?

3. How do you evaluate a model?

4. How do you handle class imbalance?

5. How do you detect data leakage?

6. How do you think about feature engineering?

7. What is model drift?

Data pipelines

8. How would you build a training-data pipeline?

9. How would you validate incoming data?

10. How would you version datasets?

11. How would you reproduce a training run?

12. How would you detect broken features?

Deployment

13. How would you deploy a model?

14. Batch vs online inference: when would you choose each?

15. How would you roll out a new model?

16. How would you roll back a bad model?

17. How would you handle model dependencies?

Monitoring

18. What would you monitor after deployment?

19. How would you detect model drift?

20. How would you detect data drift?

21. How would you investigate a sudden drop in model performance?

22. What metrics matter for a production ML system?

ML system design

1. Design a recommendation system.
2. Design a fraud detection system.
3. Design a document classification pipeline.
4. Design a real-time prediction service.
5. How would you scale inference?
6. How would you manage expensive models?
7. How would you handle latency constraints?
8. How would you think about reliability?

Behavioral

1. Tell me about an ML system you built.
2. Tell me about a model that failed.
3. Tell me about a disagreement with a data scientist.
4. Tell me about an ML tradeoff.
5. Tell me about a production incident.
6. Tell me about a time you simplified a system.
7. What ML area are you developing?
8. How do you decide when not to use machine learning?
9. How do you explain ML limitations to stakeholders?
10. Why do you want an ML engineering role?

The critical distinction

An ML engineer interview may not stop at

“Does the model work?”

You may also need to answer

Can it be deployed? Can it be monitored? Can it be reproduced? Can it scale? What happens when the data changes?

Practice the full lifecycle

data → training → evaluation → deployment → monitoring → iteration That's much closer to production thinking. [Practice an ML Engineer interview with Mockly]