OpenAI AI Engineer Interview Questions

I would build a representative test set from real traffic, include edge cases, and define scoring criteria that reflect user value. I would also make sure the evals are stable enough to compare changes over time while still being close to production behavior. Good evals are what make AI iteration disciplined instead of guess-driven.

I would minimize what is stored, protect sensitive data in logs and memory, and make sure user content is only used in approved ways. Privacy should be designed into the system from the start, especially when prompts or retrieval may expose confidential information. In AI products, privacy and trust are inseparable.

I would look for ways to reduce variance through better instructions, constrained decoding, retrieval, and postprocessing checks. If the task requires repeatability, consistency may matter more than a small lift in average score. A product usually needs dependable behavior, not just occasional brilliance.

I would use human review when the cost of an error is high or when the model still needs supervision to reach acceptable quality. The human should handle the cases that need judgment, while the model should reduce repetitive work and speed up the workflow. Good systems use humans where they add the most value, not everywhere.

I would collect explicit feedback, implicit usage signals, and failure examples, then feed that into evaluation and iteration. The loop should be structured so that product changes lead to measurable model or system improvements. Without a feedback loop, AI quality tends to drift instead of improve.