Research Engineer, Machine Learning

Mistral•Palo Alto, CA

About The Position

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. The Role 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, …) and turn fresh ideas into repeatable, scalable code.

Requirements

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.

Nice To Haves

bonus for CUDA or data-pipeline chops.

Responsibilities

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.