Research Engineer, Machine Learning

Research Engineer, Machine Learning at Mistral AI scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

ParismidFullTime

AI in this role

pytorchtensorflowjax

nlp

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.

• Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).

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

• Deliver prototypes that become production-grade components for Le Chat 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.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

NlpPyTorchTensorFlowJax

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
What's a project where you used Jax hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Nlp, PyTorch, TensorFlow, and Jax. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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