Stanford AI Lab AI & ML Engineer interview questions

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

A deceptively simple sampling question with one trap (does temperature 0 fix hallucinations?) and one senior move interviewers reward. Know which workflows want zero.

The concept underneath every RAG system you'll ever deploy. How to explain vectors, cosine similarity, and the one limitation of embeddings that costs real deployments accuracy.

A staple at OpenAI and Anthropic in both coding and design rounds. Most candidates can name the algorithms; few can say which one lets 2x your limit through, and that's the part that gets scored.

Anyone can list mitigations; the question is really about costs: latency, dollars, and lost recall. The three-layer answer with honest price tags that AI-lab interviewers score highest.

Averages lie, and interviewers know most candidates quote them anyway. The fastest way to sound production-grade in any latency discussion is to reason about percentiles correctly, including the one trap almost everyone falls into.

A classic metrics question with a domain twist. The answer isn't 'recall'; it's a cost-of-errors argument plus a threshold strategy, and interviewers grade the reasoning, not the metric you pick.

The signature FDE debugging genre: a moving fault, an opaque client, and a customer who's sure it's your fault. The winning method splits the problem at the boundary, and knows the four classic culprits hiding on their side.

The same operation scores attention and ranks your RAG results. Knowing exactly what the dot product measures, and when its magnitude term quietly breaks your retrieval, is what separates a working pipeline from a mysteriously bad one.

JSON mode, strict schemas, constrained decoding, validate-and-retry: the escalation ladder for structured output, plus the catch nobody mentions: schema-valid can still be factually wrong.

Why a raw base model answers your question with three more questions, what RLHF actually buys, and the niche cases where customers should pick base. A tidy answer most candidates fumble.