ByteDance AI & ML Engineer interview questions

Moderation is a multi-stage classification problem with harsh tradeoffs: false negatives cause real harm, false positives silence legitimate users, and the adversary keeps adapting. What interviewers reward: the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. This lays out the design.

A 22-point AUC jump is both an opportunity and a red flag. Weaker candidates cheer; sharp ones grow wary and know precisely which leakage checks belong before anything ships.

Shipping a model is itself an experiment, and this question tells apart people who run A/B tests from those who p-hack them. The signal is pre-registering the metric, sizing the test, and holding back from peeking.

The canonical AI system-design question. The signal is covering both the model lifecycle and the serving stack at real scale, without rambling. Most candidates design only half and lose the points.

The KV cache is what makes LLM serving hard, and the interviewer wants the mechanics: what it holds, why it caps concurrency, and the tricks that shrink it. This is the answer that shows you grasp decode-time economics.

Labs reach for this practical screen often, since it probes concurrency, time handling, and judgment inside 20 lines. The pitfall is a background thread that burns CPU. Below is the lazy-refill approach interviewers expect, along with the follow-ups.

A from-scratch ML-coding staple that tests whether you really know the math you rely on. The signal is the clean gradient (it reduces to Xᵀ(ŷ - y)/n), numerical stability, and vectorization. Here is the implementation and the details interviewers push on.

Everyone can write the happy path. The screen is decided by the two branches most candidates skip: what the system does when retrieval finds nothing good, and what it does when the model cites a chunk you never sent.

Imbalance turns up in fraud, churn, and abuse, and the naive answer (oversample, done) leaks data and inflates offline metrics. The signal is favoring cost-sensitive learning, fixing the metric, and resampling correctly. Here is the full toolkit with its tradeoffs.

The most common ML system design round. Interviewers reward the funnel structure: candidate generation, then ranking, then re-ranking, with the right model at each stage plus an honest plan for cold start, freshness, and feedback loops. This lays out that structure.

Models fail silently, so the real question is whether you would notice. What interviewers look for: watching the right layers (operational, data, prediction, outcome) and alerting on drift without drowning in false pages. This covers the system and the metrics that count.

Text-to-SQL is deceptively hard because correctness is all-or-nothing and the failure mode is a confident wrong number. What interviewers reward: schema grounding, query validation, and a safety layer, not merely 'prompt an LLM with the schema.' This lays out the production design.

The moment a company runs LLMs in more than one place, it needs a gateway. The signal is the cross-cutting concerns (cost, reliability, observability, governance) that a gateway consolidates, not merely 'proxy the API.' Here is the design.

Ads ranking is where calibrated probabilities collide with tight latency and money. The signal is knowing that CTR has to be calibrated (not just ranked), the feature and serving design, and the auction context. Here is the design that goes past 'train a classifier.'

Anomaly detection seems simple until seasonality, cold start, and alert fatigue arrive. The signal is modeling the expected baseline (including weekly and daily cycles), picking unsupervised methods when labels are scarce, and tuning so you do not bury users in false alarms. Here is the design.

Code migration is a high-value agentic task with a strict correctness bar: the migrated code has to still work. The signal is grounding in the real repo, transforming in verifiable chunks, and gating on tests, never one huge LLM rewrite.