Mistral AI system design interview

Round 4 of 5 · 5 questions reported

Length: 60 min
Format: Architecture discussion
They grade: Latency, throughput and scaling inference
Heads-up: Either classic (cache, key-value store, chat backend) or AI-era (LLM serving, RAG, fine-tuning infrastructure, an eval harness). A 2026 New York report, on CodeSignal: a PDF-to-Markdown inference API built from CPU, GPU and memory-heavy stages, first synchronous for one 1,000-page file, then asynchronous for many.

Questions Mistral AI asks in this round 5

Build a RAG agent on the Mistral API, then defend its designAI systemWrite a small Python tool that answers questions over a local folder of Markdown and PDF files with retrieval-augmented generation: ingest and chunk the files, embed and index the…
Last asked Jul 2026
Rapid-fire questions on LLM internalsML designA fast round of short questions on how LLMs work: transformer and attention internals, training and fine-tuning choices, and how models are run in practice. Each answer should be…
Last asked Jul 2026
Design a PDF-to-Markdown inference API (sync and async)AI systemBuild a service that turns PDF files into Markdown out of three existing functions: one that splits a PDF into page images (CPU-heavy), an OCR model that reads a page image or a…
Last asked Apr 2026
Design a multi-tenant fine-tuning and LoRA serving platformAI systemA model provider lets customers fine-tune its open-weight models on their own data through an API, then call the fine-tuned models through the same chat endpoint. Most use LoRA…
Design an LLM inference serving platformAI systemDesign the system that serves several large models to many customers with low latency and high GPU utilization.