OpenAI Machine Learning Engineer Interview Questions

I would first clarify the corpus size, freshness requirements, privacy constraints, and how accurate the answers need to be. My design would split the system into ingestion, indexing, retrieval, reranking, and generation so each part can be tuned separately. The reason I choose this structure is that RAG systems usually fail when retrieval quality, not generation quality, is the real problem. I would also include evaluation, logging, and fallback behavior for cases where the model is uncertain. If the interviewer wants more depth, I would talk about chunking strategy, embedding updates, and how I would measure answer quality.

I would use a mix of offline metrics, human review, and task-specific success criteria. For factual QA, I would look at correctness, citation quality, and whether the answer stays grounded in the retrieved sources. The reason I choose multiple evaluation methods is that a single metric often misses important failure modes. I would also think about edge cases like ambiguous questions, missing context, and answers that sound fluent but are wrong. In practice, I would want both automated checks and human judgment before shipping changes.

I would first measure where the time is going: retrieval, prompt construction, model inference, or post-processing. If the model itself is the main bottleneck, I would think about batching, smaller models, prompt reduction, or caching. The reason I start with measurement is that latency problems in ML systems often come from one expensive stage rather than the whole pipeline. I would also discuss the trade-off between speed and answer quality, because the fastest system is not useful if it becomes less accurate. If needed, I would mention hardware utilization and request routing as additional levers.

I would first define what drift means for the use case: data drift, label drift, or behavior drift. Then I would monitor input distributions, output quality, and downstream task success so I can spot when the model is changing in a harmful way. The reason I choose this approach is that drift is often subtle and only visible over time, not in a single test. I would also think about retraining triggers, rollback plans, and whether some segments of traffic are degrading faster than others. If the interviewer wants more detail, I would discuss canary deployments and comparison against a stable baseline.

I would first look at whether the issue is training data, prompt design, retrieval quality, or the model's calibration. A good fix might be to make the system more willing to say 'I do not know' when confidence is low or when retrieval support is weak. The reason I choose that direction is that overconfident wrong answers are often worse than uncertainty in a user-facing system. I would also think about better grounding, answer verification, and confidence thresholds. If the interviewer asks, I would mention that the product should prefer honesty over polished but incorrect output.