ML Engineer (MLE) interviews at FAANG are different from SWE interviews. The coding round is shorter and easier. The system-design round is harder and ML-specific. Most candidates over-prepare on LeetCode and under-prepare on ML system design — which is exactly backward.
The Five Rounds You Should Expect
Round 1: ML coding (45 min)
Python + NumPy / PyTorch + scikit-learn. Typical problems: implement K-means from scratch, implement logistic regression with gradient descent, implement a simple transformer attention block, implement a sampling algorithm. Easier than SWE coding rounds but tests numerical fluency.
Round 2: ML system design — recommendations / ranking
Most common ML system design topic. Design YouTube recommendations, design Amazon product ranking, design Spotify Discover Weekly, design Meta ad ranking. Expect: candidate generation → ranking model → re-ranking → exploration/exploitation balance. Know two-tower retrieval and DLRM cold.
Round 3: ML system design — content moderation / fraud / search
Second most common topic. Design fraud detection for a payments product, design content moderation for a social platform, design search ranking. Expect heavy emphasis on labelling strategy, online vs offline metrics, and human-in-the-loop.
Round 4: ML fundamentals + statistics (45 min)
Bias-variance trade-off, regularisation (L1 vs L2 vs dropout), bagging vs boosting, A/B testing power analysis, p-hacking awareness, transformer mechanics, attention mechanisms, embedding spaces. Conceptual, not coded.
Round 5: Behavioural + cross-functional (45 min)
ML projects involve product, engineering, data science, and ops. Behavioural rounds test: How did you handle disagreement with PM on metrics? How did you respond when your model was failing in production? How did you communicate model uncertainty to a non-technical stakeholder?
The Offline / Online Metric Bridge (Most Common Trap)
Strong MLE candidates always discuss the relationship between offline metrics (AUC, precision/recall, MAP, NDCG) and online metrics (revenue per session, click-through rate, retention). Weak candidates discuss offline metrics in isolation. Always bridge: 'Our offline AUC improved by 3%, but in the online A/B test we saw +0.5% CTR with no impact on revenue per session, suggesting the model picked up on a feature that correlates with clicks but not with purchase intent.' That phrasing is what gets you to Senior MLE.
Universal ML System Design Cheat Sheet
- Always state the ML problem framing first: regression, binary classification, multi-class, ranking, generation. The problem framing drives every downstream choice.
- Always discuss labelling strategy. Where do labels come from? Implicit (clicks) vs explicit (ratings) vs human-annotated (Mechanical Turk)? Cost vs latency vs quality trade-offs.
- Always discuss training data freshness. Daily retrain? Weekly? Online learning? Cite the trade-off between freshness and stability.
- Always discuss serving architecture. Batch inference, near-real-time, real-time? Latency budget?
- Always discuss feature stores. Online vs offline parity is the #1 source of train-serve skew bugs in production ML.
- Always discuss exploration/exploitation. Pure exploitation collapses long-tail content; pure exploration tanks short-term metrics. Multi-armed bandit framing.
- Always discuss fairness and bias. Disparate impact, demographic parity, counterfactual fairness. Even if the interviewer doesn't ask, mentioning fairness signals MLE maturity.
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