OpenAI AI Engineer Interview Questions

I would combine task success metrics, quality metrics, and safety metrics. For example, I might track answer correctness, user acceptance, refusal quality, and cost per request. A strong answer shows that I would not optimize a single metric if it creates hidden failures elsewhere.

I would use layered defenses: input filtering, safe completion rules, output checks, and monitoring for abuse patterns. If a model still produces unsafe output, I would treat it as a system failure and tighten the guardrails rather than blaming the user flow. The key is to design safety into the product, not bolt it on afterward.

I would compare the training or test distribution with live traffic to see where the mismatch is happening. Often the problem is that production prompts are messier, more ambiguous, or more adversarial than the evaluation set. I would then update the evals so they match reality more closely.

I would look at calibration, robustness, refusal behavior, latency, cost, and user satisfaction. Accuracy alone can hide important issues like brittle behavior on edge cases or poor performance under noisy inputs. For an AI product, quality is really a multi-dimensional trade-off.

I would use tools when the model needs reliable external actions or data it should not guess. That might include search, databases, calculators, or internal APIs. The main design challenge is making tool invocation safe, observable, and recoverable when something fails.