Associate Director / Senior Manager — Real-World Interview Questions

Target Role: Associate Director / Senior Manager at Genentech: Biomedicines
Focus Areas: Leadership, Behavioral, MLOps, Distributed Training, ML Performance

Behavioral / Resume Questions

1. Tell me about yourself.
2. Why Genentech: Biomedicines?
3. Your technical team disagrees with your decision — what do you do?
4. If the decision is not quantitative/qualitative and does not go your way, how do you respond?
5. Talk about a time when you noticed and solved a problem.
6. Talk about a project where success criteria were not clearly defined.
7. If you own a data generation platform and stakeholders have conflicting requirements, how do you manage them?
8. What would engineers say about you in the last 12 months?
9. Give an example where you changed your opinion after hearing other engineers.
10. Coming from Amazon — what did you like and not like?
11. If you join a company with immature processes, how would you help improve them?
12. When was the last time you studied biology?
13. What drives you outside of work?
14. Do you have any questions for the manager?

Project Deep Dive Questions

1. Fraud detection project deep dive — walk through the full lifecycle.
2. What was the ML platform domain for fraud detection?
3. What did the fraud data look like?
4. What surprised you in the fraud detection data?
5. How would you improve inference optimization for large-scale data at Amazon?

MLOps / ML Platform Questions

1. What parts of MLOps excite you most?
2. How do you guarantee the same system execution order and reproducible results?
3. How do you debug distributed training problems?
4. How do you pinpoint exact optimizations during debugging?
5. Describe your experience with scaling training beyond SageMaker data parallelism.
6. Why is documentation important in ML systems?
7. How do you approach product engineering in an ML context?

Distributed Training / ML Performance Questions

1. Describe a transformer model scaling issue you faced — for example, across 4 nodes and 32 GPUs.
2. How do you handle model imbalance in distributed training?
3. What tools do you use to debug distributed training bottlenecks?
4. How does MLflow help detect performance issues?
5. How do you address class imbalance in fraud detection?
6. What are the minority classes in fraud detection datasets, and how do you deal with them?

Conceptual / Communication Questions

1. How do you turn a technical story into something interesting for customers or stakeholders?
2. Why is experimentation pace high in ML research environments, and how do you support it?
3. What tools are you comfortable using to debug training issues?

Last updated: March 2026