All roles / Mistral AI / Research Engineer, Machine Learning

Research Engineer, Machine Learning

Evals & Benchmarks2w ago

Source verified: this exact posting was present on Mistral AI’s careers feed on 2026-10-09. View employer source →

What the posting asks for

Names: PyTorch, JAX, TensorFlow, Kubernetes, Python
Doctorate: Mentioned, without saying whether it is needed

Read out of the employer's own description. Absence means the posting does not say, not that the answer is no.

The role

About Mistral

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

About the Research Engineering team

The team spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve.

As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power our open-weight models. Working hand-in-hand with Research Scientists, you’ll either join:

- Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or

- Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, Safety …) and turn fresh ideas into repeatable, scalable code.

What You Will Do

• Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.

• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.

• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.

• Deliver prototypes that become production-grade components for Vibe and our enterprise API.

What We're Looking For

• Master’s or PhD in Computer Science (or equivalent proven track record).

• 4 + years working on large-scale ML codebases.

• Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).

• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.

• Strong software-design instincts: testing, code review, CI/CD.

• Self-starter, low-ego, collaborative.

What We Offer

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page.

Privacy Policy

Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.

Published by Mistral AI on their own careers page and reproduced here unedited. Read it at Mistral AI.

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What this listing does not tell you

Listed 11 days, which is recent for this board. Of the 114 evals and benchmarks roles this board has watched from listing to removal, 20% were gone from their employer's careers page by day 11, and the median came down after 56 days. That is a description of other listings that have already ended, not a prediction about this one: this board records when a listing disappears, never why, and a posting still up is not on a clock it can see.

Pay is not confirmed for this role. Check the employer’s posting for current compensation. Explore published bands from other roles →

Mistral AI has 17 roles open on this board, 11 of them in evals and benchmarks.

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