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MLOps Engineer — Real-World Interview Questions

Target Role: MLOps Engineer at Genentech: Biomedicines
Focus Areas: ML Pipelines, Distributed Training, Fraud Detection, Biology / Drug Discovery, Agentic AI

Resume / Personal Questions

  1. Tell me about yourself.
  2. What ML projects have you worked on?
  3. Explain your fraud detection project.
  4. Explain the RAG chatbot project.
  5. Explain the drift detection platform.
  6. What was your role in building the ML platform?

Fraud Detection / ML Model Questions

  1. What model architecture did you build from scratch?
  2. How did you construct the fraud detection model from scratch?
  3. Which ML frameworks did you use (PyTorch, XGBoost, etc.)?
  4. How did you train the model?
  5. What features were used in fraud detection?
  6. How did you handle class imbalance?
  7. Did you try resampling techniques?
  8. Which resampling approach did you choose and why?
  9. How did you implement weighted loss?
  10. How did you improve model training stability?
  11. What happens if the loss becomes very large?
  12. How did you optimize the model?
  13. What limitations or tricky issues did the model have?

ML Infrastructure / Platform Questions

  1. How do you collaborate with ML scientists?
  2. How do you communicate requirements between ML scientists and engineers?
  3. After requirements are defined, what happens next in the ML lifecycle?
  4. How do you build inference pipelines?
  5. How do you ensure reproducibility in ML workflows?
  6. What tools do you use for experiment tracking and model architecture?

Distributed Training / Scale Questions

  1. What scale of dataset did you train on?
  2. How do you train models in distributed or parallel environments?
  3. What architectures scale well for large datasets?

Biology / Drug Discovery Questions

  1. How is machine learning used in drug discovery?
  2. Why use generative models in biology?
  3. What problems does AI solve in drug development?
  4. How do ML platforms help scientists in biological research?

Collaboration Questions

  1. What questions should engineers ask ML scientists?
  2. How do engineers support researchers without slowing down experiments?

AI Engineering / Agentic Workflow Questions

  1. What is agentic AI in coding or engineering workflows?
  2. How can agentic systems help ML pipelines?

Last updated: March 2026