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Mistral AI interview notes, page 2 of 2

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Screna has 16 Mistral AI interview write-ups. The most reported round is the technical phone screen (7).

16 interview notes · updated Jul 2026

Mistral AI·Software Engineer·Technical Phone Screen

Jun 2026
Did a technical screen for a software engineer role at Mistral AI. The whole thing was one multi-part algorithmic problem built around GPU cluster scheduling, which I thought was a clever framing for a DP question but also kind of brutal in how it escalated.
  • You have n GPU clusters each with a fixed daily capacity. Given usage records per day per cluster, compute the remaining capacity of each cluster at the end of every distinct day that appears in the input, returned as a 2D array in ascending day order.
  • Given the remaining cluster capacities per day from part 1, find the maximum total GPUs a workload can access if it can freely pick any cluster each day with no switching limit.
  • Same setup as part 2, but now the workload can switch clusters at most K times total across all days. Maximize the total GPUs accessed.

“Pretty straightforward setup question, basically just grouping and subtracting.”

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Mistral AI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Mistral AI coding round with a multi-part GPU scheduling problem. The second part was a follow-up that felt almost too easy after the first.
  • You have a 2D array where each row is a day and each column is a cluster, with values representing remaining GPUs. A workload can freely switch clusters between days. What is the maximum total number of GPUs the workload can use across all days?

“This was a follow-up to a harder part 1 and I almost overthought it.”

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Mistral AI·Software Engineer·Take-home Assignment

Jun 2026
Take-home style setup task for a Software Engineer role at Mistral AI. The whole thing was basically 'prove you can wire up a clean Python environment and hit our API without making a mess of it.' More practical than I expected, less algorithmic.
  • Walk through how you would set up a reproducible local Python environment for a Mistral-based agent, including version management, virtual environments, dependency locking, secret handling, and verifying the setup with a real API call.

“This is deceptively broad.”

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Mistral AI·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed for a PM role at Mistral AI. One question about past product launches or scaling work. Short and hard to read in terms of signal.
  • Walk me through a product you launched or scaled. What was your specific role in it?

“I went with a scaling story rather than a launch because I thought it showed more operational depth.”

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Mistral AI·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Interviewed for an ML Engineer role at Mistral AI and got deep into transformer internals pretty fast. The whole session felt like a whiteboard lecture you weren't fully prepared to give, covering everything from Q/K/V shapes to sampling strategies.
  • Walk me through the Transformer architecture in detail, including attention mechanisms, positional encoding, and how the encoder and decoder differ in their usage.
  • Describe the tensor shape flow through the Q, K, and V projections in a self-attention layer.
  • How would you implement top-p (nucleus) sampling using softmax during LLM decoding at a single generation step?
  • How does FlashAttention reduce memory traffic compared to standard attention?
  • What changes in the attention mechanism when you move from self-attention to encoder-decoder cross-attention?
  • What kinds of bugs can cause LLM decoding to become nondeterministic or produce degenerate outputs?

“I started with the residual stream and attention heads, which felt natural, but then they pushed on positional encoding and I fumbled a bit between absolute positions and RoPE without being asked to compare them.”

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Mistral AI·Machine Learning Engineer·Onsite - Coding / Algorithms

Jul 2026
Live coding round at Mistral AI for an ML Engineer role. The whole thing was building a small RAG or agent system from scratch using a provided API token, which sounds manageable until you're actually doing it under interview pressure.
  • Given an API token for an LLM backend, design and build a minimal agent or retrieval-augmented generation tool live. Walk through your architecture and implementation plan.
  • Before building, what clarifying questions would you ask about the requirements?
  • How would you evaluate the quality of answers produced by your system?
  • How would you add conversation memory to this system?
  • How would you stream responses from the LLM back to the user?
  • How would you design the system so you can swap out LLM providers without rewriting your core product logic?

“This is the core ask and it's deceptively open-ended.”

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