Meta Machine Learning Engineer Interview Questions

I would define task-specific checks first, because automated evaluation only works when the success criteria are clear. Then I would combine rule-based checks, reference comparisons, and model-based judges where appropriate. The reason I choose a multi-layer evaluation pipeline is that one judge rarely catches every failure mode. I would also think about evaluator bias, calibration, and when human review is still required. If the interviewer wants more detail, I would describe how I would track evaluation drift over time.

I would first ask whether the problem is missing knowledge or missing behavior. If the model needs fresh or private information, I would usually choose RAG because it keeps knowledge external and easier to update. If the model needs to learn a style, format, or task pattern, fine-tuning can be the better choice. 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 pick the smallest change that solves the real issue.

I would first decide which feedback signals are trustworthy, because not all user reactions are equally useful. Then I would log inputs, outputs, and feedback in a privacy-safe way so the data can be reviewed and used later for training or evaluation. The reason I choose this approach is that model improvement depends on learning from real failures, not just benchmark data. I would also think about label quality, bias, and how to avoid overfitting to noisy feedback. If the interviewer wants more depth, I would describe a curation pipeline for turning feedback into training examples.

I would make sure the system has clear metrics, versioning, rollback, monitoring, and a reliable data pipeline. I would also confirm that the model is evaluated both offline and online and that there is a plan for drift, failures, and misuse. The reason I choose this checklist is that a strong model by itself is not enough; the surrounding system has to be dependable too. I would also think about privacy, safety, and cost before calling the system production-ready. In an interview, I would emphasize that production readiness is about the full pipeline, not just the model artifact.

I would first check whether the labels and objective are biased toward immediate engagement instead of long-term value. Then I would adjust the loss, add regularization, and evaluate with metrics that reflect the real product goal rather than only click-through rate. The reason I choose this approach is that short-term clicks can make a model look successful while hurting user satisfaction later. I would also think about exploration, feedback loops, and delayed outcomes. If the interviewer wants more depth, I would discuss counterfactual evaluation and debiasing.