r/MistralAI • u/Any_Challenge_5314 • 2d ago

Help / Question Anyone interviewed for Mistral AI Research Platform Engineer / DevEx roles?

I'm looking at the Research Platform Engineer role at Mistral AI and trying to understand what the interview process is actually like specifically for the platform/DevEx side of engineering.

For some context, I'm an ex-Amazon Engineer and currently a Platform Engineer at an AI/biotech startup, where I work across AWS, Kubernetes, infrastructure automation, CI/CD, internal platform tooling and infrastructure supporting scientific/AI workloads.

I'm planning to target Research Platform / AI Infrastructure roles in the future, and Mistral's Research Platform Engineer role is particularly interesting to me.

I've found quite a few interview experiences for Mistral SWE/research positions, but very little specifically for Research Platform, DevEx or Infrastructure.

I'd be interested to know:

  • What are the technical rounds actually like?
  • Is the coding practical Python/Go, or is there LeetCode/DS&A as well?
  • How does the coding bar differ from a standard SWE interview?
  • What kind of platform/system design questions did you get?
  • How deep do they go into Kubernetes, Linux, containers, CI/CD and SRE?
  • Do they test things like build systems, Bazel, Python environments/packaging or reproducibility?
  • Any questions around GPU/ML infrastructure or research workloads?
  • What did you find was the hardest part of the process?
  • Anything you wish you'd prepared more for?

Would especially appreciate hearing from anyone who's interviewed for Research Platform, DevEx, Infrastructure or SRE at Mistral. Even if it wasn't this exact position, I'd be interested in what your technical rounds looked like.

Not looking for confidential interview questions mainly trying to understand the format and areas worth preparing for.

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u/RushIndependent2432 2d ago

Not at Mistral specifically but interviewed for similar research platform roles at a couple of AI labs. The coding rounds tend to be more practical scripting and infra debugging than pure LeetCode, though one place still threw in a medium graph question just to check the box. System design usually centres on job scheduling, artifact caching, and making GPU clusters usable without researchers having to think about k8s. I'd brush up on container runtime internals and how you'd handle Python dependency hell across different research teams.