Get the free AI Engineer Interview Question Bank

The average US total comp for an ML/AI software engineer is $245,000 (levels.fyi) — and between you and that paycheck is one brutal interview. Instead of guessing what they ask, this bank is built from hundreds of REAL candidate-reported questions: dated interview reports on prachub (OpenAI's 'Debug a Broken Transformer' and KV-cache rounds, Anthropic's GPU-deployment and batched-inference designs, Meta's LLM-assistant designs, the Amazon RAG screen captured verbatim), the interviewing.io company guides ('Design a Claude chat service', and the values round most candidates fail), a 1,896-star GitHub bank of LLM questions reported at Google/NVIDIA/Meta/Microsoft, Exponent guides, and first-person Reddit threads. Inside the 13-page bank: all 50 questions organized by round (LLM & transformer internals, RAG & retrieval, system design & agents, coding rounds, production & evals, behavioral & values), each with what the interviewer is actually probing and its source; model-answer sketches for the 12 questions that decide offers — including the 5 from the video (KV cache, quantization, Design a Claude chat service, hallucination detection in production, and 'Why should we NOT hire you?'); verified market numbers ($245K average, level bands, lab process lengths, DeepMind's sub-1% acceptance); and every source linked so you can go deeper. Honest sourcing throughout: candidate-reported means reported at, not company-confirmed — and it's the closest thing to the real question list you can get.

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