OpenAI Machine Learning Engineer Interview Questions

I would first check whether randomness, temperature, sampling settings, or nondeterministic hardware behavior is responsible. Then I would isolate whether the inconsistency comes from the model itself, the prompt, or upstream retrieval and preprocessing. The reason I choose this approach is that reproducibility problems often come from hidden randomness in one stage of the pipeline. I would also think about seeding, logging exact prompts, and versioning the model and data. If needed, I would explain how I would create a reproducible test harness.

I would first ask whether the issue is missing knowledge or missing behavior. If the model needs access to fresh or private information, RAG is usually the better fit because it keeps the knowledge external and up to date. If the model needs to change its style, format, or task behavior, fine-tuning can be more appropriate. The reason I compare them this way is that they solve different problems even though both can improve results. In an interview, I would say I would choose the smallest intervention that solves the actual problem.

I would monitor input distribution, output quality proxies, latency, error rates, and user feedback signals. I would also build segment-level dashboards so I can see whether the problem is concentrated in one type of traffic. The reason I choose segment monitoring is that aggregate metrics can hide important regressions. I would also think about alerting thresholds, canary comparisons, and periodic human review. If the interviewer wants more depth, I would mention data and model version tracking so regressions are easier to trace.

I would assume the user input and any retrieved content may be adversarial. My design would separate trusted system instructions from untrusted content and apply strict boundaries when passing data into the model. The reason I choose this approach is that prompt injection is often a data trust problem, not just a model problem. I would also think about tool access controls, content filtering, and making sure the model cannot blindly follow instructions from retrieved text. If the interviewer wants more detail, I would mention logging and red-team testing for attack discovery.

I would first measure where the cost is coming from: model size, context length, batching inefficiency, or low GPU utilization. Then I would consider smaller models, caching, distillation, prompt trimming, or smarter routing to cheaper models for simpler requests. The reason I choose this approach is that cost problems usually improve when the system stops spending the same amount on every request. I would also think about quality trade-offs and whether premium traffic should get different handling than low-risk traffic. If the interviewer wants more detail, I would discuss scaling policies and queue prioritization.