infrastructure
Posted Apr 21Research Engineer, Data Infrastructure
at Mistral AI
On-site
Responsibilities
- Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
- 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. About you
Requirements
- About Mistral At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.
- 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.
- Join us to be part of a pioneering company shaping the future of AI.
- See more about our culture on https://mistral.ai/careers.
- As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power our open-weight models.
- Master’s or PhD in Computer Science (or equivalent proven track record).
- Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
Experience
- 4 + years working on large-scale ML codebases.
Benefits
- Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
Additional details
- Our technology is designed to integrate seamlessly into daily working life.
- Our diverse workforce thrives in competitive environments and is committed to driving innovation.
- Our teams are distributed between France, USA, UK, Germany and Singapore.
- Role Summary 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.
- 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 do
- Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
- Strong software-design instincts: testing, code review, CI/CD.