10 X Machine Learning Engineer (New Grad) Interview Questions (2026)

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Pick one project. Cover problem framing, data, modeling, evaluation, and what you'd do differently. Have specific numbers: dataset size, baseline vs final metric, online performance if you have it. Surface-level course projects lose; depth in one applied project wins.

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Be specific: the recommendation problem at X's scale is genuinely hard (cold start, real-time signals, billions of items, adversarial content). What draws you to it? Mention what you've read or seen about the For You algorithm. Vague 'I love ML' answers fail.

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STAR. Pick a real story (training/serving skew, data drift, label leakage, distribution shift). Cover how you noticed (which metric flagged), how you isolated the cause, and what you fixed. If you don't have one, frame a course project or competition with the equivalent dynamics. Don't fabricate.

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Represent each vector as a hash map (index → value) or a sorted list of (index, value) tuples. Hash map: iterate the smaller, look up in the larger, sum products. O(N) where N is the smaller. Sorted lists: two pointers, advance based on index comparison. Discuss tradeoffs: hash map is faster per query, sorted is more memory-efficient and traversal-friendly.

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Naive: compute cosine similarity to all items, min-heap of size K. O(N * d log K). Walk through normalization — pre-normalize embeddings so cosine becomes dot product. Discuss ANN alternatives (HNSW, IVF) at scale and the recall/latency tradeoff.

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User tower encodes user features (history, demographics, context) to an embedding. Item tower encodes content features to an embedding in the same space. Score = dot product. Training: positive pairs are real interactions, negatives are sampled (in-batch is common). At serve time: pre-compute item embeddings, online encode the user, ANN lookup for top-K. Discuss the cold-start problem and how you'd address it.

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Two-stage: candidate generation (cheap, broad — retrieves a few hundred candidates per user) and a ranker (expensive, scored). Features: user history, post freshness, author affinity, engagement signals. Discuss the label problem (what counts as positive — click? dwell? engagement? a mix?), online vs offline training, and the cold-start case for new users or new posts.

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Multi-stage: offline (NDCG, MAP, AUC on held-out data — check for distribution shift), shadow (run the new model alongside the production one and compare), A/B test (small percent of traffic, primary metrics tied to the business: session length, engagement rate, retention). Discuss the limits of each: offline metrics often don't predict online wins.

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Loss weighting (give minority class higher loss weight) is the easy first move. Over-sampling (SMOTE, simple replication) for the minority class or under-sampling the majority. Threshold tuning at inference (the default 0.5 is rarely right). Mention focal loss as a more sophisticated alternative. Discuss why accuracy is the wrong metric (use AUC, precision-recall AUC, or F1).

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Concept-level. Logging pipeline captures interactions with full context (features used, predictions, outcomes). Training job consumes the log stream — could be mini-batch (every N minutes) or true online (one example at a time, less common). Discuss feedback loops (the ranker influences what users see, which influences training data), the cold-start failure mode for new content, and how you'd detect when the model degrades.