Mistral AI Interview Questions

Ten questions from Mistral AI, and the model lab shows in every one. ML system design and machine learning account for 6 between them: designing a PDF-to-Markdown inference API, a quick-fire round on LLM systems, and a build task where you assemble a small agent or RAG tool against the Mistral API itself. The transformer question runs both ways, asking you to explain the internals and then implement top-p decoding, so knowing the sampling maths is not enough if you cannot write the loop. Capacity planning appears as a puzzle on computing remaining GPUs under switching limits, which is a real constraint for a company that trains its own models. One round asks how you actually use an AI coding assistant, a pointed question from a firm selling the models underneath those tools. Software Engineer accounts for 6 of the roles against 4 for Machine Learning Engineer, 7 questions come from technical screens, and 4 are rated hard. Nine of the ten arrive with a worked answer, and none of the page is gated.