A hiring manager's question bank for ML engineers — the bias-variance tradeoff, honest model evaluation, feature engineering, and getting models into production. Built to separate people who can fit a notebook model from people who ship reliable ML systems.
A machine learning engineer sits between data science and software engineering, and the best interviews test both halves plus the judgement that connects them. Plenty of candidates can call fit and predict on a library; far fewer understand why a model that scored 99% in a notebook fails in production. The most valuable signal is honest evaluation: does the candidate understand overfitting and the bias-variance tradeoff, know why accuracy is misleading on imbalanced data, and reach for precision, recall, F1, ROC-AUC, or a confusion matrix depending on the cost of each error type? Equally important is data discipline — data leakage, train/validation/test splits, cross-validation, and the fact that most real ML gains come from features and data quality, not exotic architectures. Because they are engineers, not just modelers, you also want the MLOps story: reproducible pipelines, model versioning, monitoring for data and concept drift, and what happens when a model that was fine last month quietly degrades because the world changed. Strong candidates think in terms of the whole system — how a model is served, retrained, and rolled back — not just the offline metric. The questions below run from ML fundamentals through evaluation and feature engineering into deep learning and production. Pair a couple of fundamentals questions with one "your model looks great offline but fails live" scenario and one deployment discussion, and you will quickly tell whether someone has shipped ML into a real product or only competed on a leaderboard.
Pick six to eight questions across two or three categories rather than the full list. Start with an ML Fundamentals question, then spend real time on an Evaluation & Data scenario — the "great offline, bad in production" discussion is gold — and one Production & MLOps topic. Follow every clean answer with "why that metric?" or "how would that break in production?" to see true depth.
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