Meta Machine Learning Engineer Interview Questions

I would separate offline feature computation from online serving while keeping the definitions consistent. The store should support versioning, schema validation, point-in-time correctness, and low-latency access for inference. The reason I choose this structure is that training-serving skew is one of the most common production ML bugs. I would also think about backfills, freshness, and lineage so we can trace where each value came from. If the interviewer wants more depth, I would discuss missing-value handling and access control.

I would use ranking metrics such as NDCG, MAP, recall@K, and precision@K depending on the product goal. I would also consider business metrics like click-through rate, conversions, or retention if the ranking affects a user flow. The reason I choose multiple metrics is that ranking quality has more than one dimension, and a single number rarely captures it all. I would pair offline metrics with online experiments whenever possible. If the interviewer wants more detail, I would explain why the metric should match the user experience we want to improve.

I would avoid accuracy as the main metric because it can be misleading when positives are rare. My approach would include class weighting, resampling, threshold tuning, and evaluation with precision, recall, PR-AUC, or a cost-sensitive metric based on the product objective. The reason I choose these tools is that the model should optimize the right trade-off, not just the majority class. I would also think about calibration and whether the label distribution changes over time. If the interviewer wants more depth, I would mention time-based validation and segment analysis.

I would first determine whether the issue comes from missing knowledge, weak grounding, or poor prompting. A strong fix is to improve retrieval, narrow the scope of the answer, and require evidence when the system makes factual claims. The reason I choose this path is that LLMs tend to invent details when they are forced to guess. I would also think about confidence thresholds, refusal behavior, and post-generation validation. If the interviewer wants more depth, I would mention evaluation on factual consistency.

I would monitor input distributions, output distributions, latency, and downstream business metrics so I can detect both technical and product regressions. I would also break the analysis down by segment because aggregate averages can hide important problems. The reason I choose this monitoring strategy is that drift is often gradual and easy to miss if you only look at final outcomes. I would think about alerting, canary comparisons, and retraining triggers. If the interviewer wants more depth, I would explain how to distinguish data drift from concept drift.