did this round about two months ago for a senior role roughly equivalent to L5 at bigger companies. sharing specifics because the generic 'design a URL shortener' prep is not what you need here.
the system design interview at Mistral AI is genuinely AI-infrastructure focused. the prompt I got was essentially: design a serving infrastructure for a large language model that needs to handle variable-length requests with latency SLAs under a given token-per-second budget. not the standard 'design Twitter' question.
what they actually cared about: inference batching strategies: continuous batching vs. static batching, trade-offs for tail latency KV cache: what it is, how you'd manage it under memory pressure, what eviction looks like load balancing: not just round-robin, they wanted discussion of routing based on context length or model state failure modes: what happens when a GPU node falls over mid-inference, how do you handle partial completions cost: they explicitly asked me to reason about GPU cost per query and where the optimization levers are
I'm ex-FAANG, I'd done a lot of generic distributed systems prep. that helped with the foundations but I had to pivot on the fly to AI-specific concerns. if you're coming from a traditional backend background, spend time reading about model serving frameworks: vLLM, Triton Inference Server, that kind of thing. you don't need to know them deeply but the vocabulary matters.
the interviewer was an actual senior engineer who works on inference infrastructure. they weren't reading from a rubric. the conversation went 55 minutes straight without a pause, which was rare and honestly kind of energizing.
one thing I'd flag: they push on scale pretty early. don't wait to be asked 'what if you had 10x the traffic.' lead with it.