Research Engineer, Machine Learning
resume databasePalo Alto (CA)
About the role
At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.
We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise as well as personal needs. Our offerings include Le Chat, La Plateforme, Mistral Code and Mistral Compute – a suite that brings frontier intelligence to end-users.
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 France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.
Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers.
Role Summary:
About the Research Engineering teamThe team spansPlatform(shared infra & clean code) andEmbedded(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, …) and turn fresh ideas into repeatable, scalable code.
What will you doAccelerate 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 inPython.
Deliver prototypes that become production-grade components for Le Chat and our enterprise API.
About you:
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.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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