Mistral AI MLE Interview Questions (2026)

Questions reported from Mistral AI machine learning engineer interviews. This bank tracks 12 questions asked at Mistral AI (9 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

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#43 Describe the KV cache, derive its memory formula, and discuss what it costs at production scale. ★
#44 Contrast multi-query and grouped-query attention with standard multi-head attention, and name what is sacrificed. ★
#51 Describe mixture-of-experts and how it grows model capacity without growing per-token compute. ★
#63 Describe Mistral 7B's grouped-query plus sliding-window attention and what each choice gains and costs.
#64 Explain how a Mixtral-style sparse MoE works and why a ~47B-parameter model runs at roughly 13B cost.
#89 Define catastrophic forgetting and describe how to mitigate it during fine-tuning. ★
#90 Give a decision framework for choosing between prompting, RAG, and fine-tuning, with cost and latency attached. ★
#91 Do the GPU memory math for full fine-tuning a 7B model in bf16 with Adam, then redo it with LoRA. ★
#99 After fine-tuning for a customer, target accuracy rose but the model got worse at everything else — explain what happened and how you respond.
#166 Explain continuous (in-flight) batching and why it replaced static batching. ★
#170 Compare FP16, BF16, FP8, INT8, INT4, and FP4 for serving and name what breaks at each step down. ★
#298 Implement FlashAttention-style tiled attention without ever materializing the full N×N matrix. ★