Stanford AI Lab AI & ML Engineer interview questions

Nearly every candidate can recite softmax(QKᵀ/√d_k)V. What the interviewer is after is the variance derivation and the precise training failure the scale heads off. This is the response that tells rote recall apart from real grasp.

Alignment has become a baseline expectation even beyond 'safety' roles. The interviewer is looking for the three-stage RLHF pipeline and a sharp account of why DPO threw out the reward model completely. Here is the answer that leads with mechanism, not the buzzwords.

Attention is permutation-invariant, so with no position information a transformer cannot detect word order. The signal is knowing why, and why the field shifted from absolute sinusoidal encodings to relative ones like RoPE and ALiBi that extrapolate to longer contexts.

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.

The most frequently asked applied GenAI question, and the one most candidates turn into a definition dump. What the interviewer wants is a decision framework with a default and the conditions that override it. Here is the call that earns points.

Evaluation is the toughest and most underrated part of shipping LLMs. The signal is understanding why public benchmarks mislead, why LLM-as-judge carries bias, and how to build a task-specific eval you can genuinely trust.

A tidy fundamentals question that trips up anyone who only knows GPT. The signal is pairing each architecture to its task through its attention pattern, and explaining why the field settled on decoder-only even for tasks that are not generation. Here is that answer.

Tokenization reads like plumbing until it accounts for your bill, your latency, and why the model stumbles on numbers and rare languages. The signal is understanding how subword tokenizers operate and the consequences that follow from them.

Every LLM product tunes these knobs, and the interviewer wants to hear what each one does to the probability distribution and which setting suits which task. That includes why factual and creative tasks call for opposite settings.

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.

MoE explains why some frontier models carry huge parameter counts yet serve cheaply. The signal is the sparse-activation idea, the routing mechanism, and the operational cost (memory, load balancing) that the FLOP savings quietly conceal.

PEFT is a family, not only LoRA, and interviewers test whether you know how each one injects trainable parameters. The signal is where each method adds capacity and the latency and quality tradeoffs that result.

One triangle of negative infinity, added before the softmax, is what makes transformer training parallel and generation stubbornly serial. Interviewers want the mechanism and the asymmetry it creates, not a definition of attention read back to them.