OpenAI Machine Learning Engineer Interview Questions

I would start by defining quality filters, deduplication rules, and privacy safeguards. Then I would build ingestion, cleaning, normalization, and labeling steps so the training set is consistent and auditable. The reason I choose this pipeline approach is that model quality depends heavily on the data quality upstream. I would also think about sampling bias, toxic content filtering, and versioning so training runs can be reproduced later. If the interviewer wants more depth, I would explain how I would track which data actually changed a model's behavior.

I would first check batch size, sequence length, model size, activation memory, and whether gradients or optimizer state are larger than expected. The reason I start there is that GPU memory issues usually come from a few predictable sources. I would also consider mixed precision, gradient checkpointing, sharding, and whether the input pipeline is accidentally keeping extra tensors alive. If needed, I would compare the trade-off between memory savings and training speed. In an interview, I would want to show that I can debug both the model and the training system.

I would design the serving path around batching, caching, and routing so the GPUs stay busy without hurting latency too much. My system would accept requests, group compatible ones into micro-batches, and send them to an inference fleet with clear routing and fallback rules. The reason I choose this approach is that model serving is usually limited by cost and GPU utilization as much as by raw inference speed. I would also discuss model versioning, graceful degradation, and what happens when capacity is full. If the interviewer wants more detail, I would talk about queueing, prioritization, and request deadlines.

I would first compare the offline evaluation data to the real production traffic to see whether the test set is representative. Then I would inspect logging, user behavior, and failure cases to find where the distribution differs. The reason I choose this approach is that offline metrics can look great while missing the conditions that matter in production. I would also think about feedback loops, delayed labels, and whether the model is being used in a way that the offline benchmark does not reflect. If the interviewer asks, I would mention shadow deployment or canary testing as a safer rollout path.

I would treat safety as a separate layer with its own rules, models, and evaluation. The pipeline would check prompts and outputs for policy violations, high-risk content, and adversarial inputs before the result reaches the user. The reason I choose a layered design is that no single filter catches every unsafe case. I would also think about false positives, appeals, and how to keep the system useful without blocking harmless content. If the interviewer wants more depth, I would discuss monitoring and continuous red-team testing.