Interview
Questions
OpenAI AI Deployment: Field Evidence and Reusable Systems
Practice explaining customer delivery, reproducible evaluation, feedback to Research and Product, and reusable deployment systems. Original practice informed by official role requirements.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
How do you approach evaluating whether AI is the right solution for a given problem?
Tell me about an AI or ML project you have shipped to production. What challenges did you face?
How have you evaluated LLM outputs for quality in production? What metrics or approaches did you use?
A teammate reports a large inference speedup after changing both the serving configuration and the test workload. What would you require before accepting the claim, and how would you turn the result into a reproducible regression check?
Two customer deployments reveal the same model limitation. How would you build an evidence-backed recommendation for the Research and Product teams?
A solution works for one customer, and another team wants to reuse it. How would you decide what to turn into a shared tool?
Tell me about a time when you had to balance shipping quickly with maintaining code quality or safety considerations. How did you make that trade-off?