OpenAI Machine Learning Engineer Interview Questions

I would define task-specific checks first, because automated evaluation only works well when the success criteria are clear. Then I would combine rule-based validation, reference comparisons, and model-based judges where appropriate. The reason I choose multiple evaluators is that one checker rarely catches all important failures. I would also think about bias in the evaluator, calibration, and whether human review is still needed for the hardest cases. If the interviewer asks, I would explain how I would track evaluation drift over time.

I would start by clarifying isolation requirements, latency targets, and whether tenants need custom models or just shared serving capacity. My design would include tenant-aware routing, quotas, model version control, and resource isolation so one customer does not affect another. The reason I choose this structure is that multi-tenant ML systems fail when noisy neighbors or bad deployments impact everyone. I would also think about billing, observability, and safe rollback for tenant-specific changes. If the interviewer wants more depth, I would discuss scheduling and fairness across tenants.

I would first determine whether the hallucination is due to missing context, poor retrieval, weak prompting, or an actual model limitation. A practical fix is to improve grounding by retrieving better sources, narrowing the answer scope, and asking the model to cite evidence. The reason I choose this approach is that hallucination usually gets worse when the model is forced to guess. I would also think about confidence-based refusal, answer verification, and post-generation checks. If the interviewer wants more detail, I would mention evaluation on factual consistency.

I would start by checking whether the data, training code, hyperparameters, or environment changed. Then I would compare the failed run against a known good baseline and look for differences in preprocessing, shuffling, optimizer state, or random seeds. The reason I choose this approach is that regressions usually come from a small set of change points. I would also think about whether the evaluation script itself is correct, because sometimes the model is fine and the metric is broken. If needed, I would explain how I would make the run more reproducible.

I would keep the previous model version available and make deployment changes reversible through versioned routing. The serving system should be able to shift traffic back to a stable model quickly if quality or latency worsens. The reason I choose this approach is that ML deployments need the same rollback discipline as regular software, but with extra attention to quality metrics. I would also think about compatibility of embeddings, prompts, and downstream consumers. If the interviewer wants more detail, I would discuss canary rollout and shadow traffic before full release.