About the job
About Mistral AI
At Mistral AI, we harness the transformative power of artificial intelligence to streamline tasks, save valuable time, and foster enhanced creativity and learning. Our innovative technology is crafted to effortlessly integrate into everyday work environments.
We are committed to democratizing AI by offering high-performance, optimized, open-source models, products, and solutions. Our extensive AI platform caters to both enterprise and individual needs, featuring products like Le Chat, La Plateforme, Mistral Code, and Mistral Compute, creating cutting-edge intelligence accessible to all users.
As a vibrant and collaborative team, we are driven by our passion for AI and its potential to revolutionize society. Our diverse workforce excels in competitive settings and is dedicated to fostering innovation. With teams distributed across France, the USA, the UK, Germany, and Singapore, we pride ourselves on our creativity, humility, and team spirit.
Join us in shaping the future of AI at a pioneering company. Together, we can create a lasting impact. Discover more about our culture at https://mistral.ai/careers.
Role Overview
About the Research Engineering Team
The Research Engineering team operates across Platform (shared infrastructure & clean coding practices) and Embedded (integrated within research squads). Our engineers have the flexibility to navigate the research↔production spectrum as their interests and needs evolve.
As a Machine Learning Research Engineer, you will be responsible for building and optimizing large-scale learning systems that underpin our open-weight models. Collaborating closely with Research Scientists, you may join either:
- Platform RE Team: Focus on enhancing our shared training frameworks, data pipelines, and tools utilized across all teams; or
- Embedded RE Team: Become part of a research squad (Alignment, Pre-training, Multimodal, etc.) to turn innovative ideas into scalable, repeatable code.
Key Responsibilities
• Support researchers by managing the complex aspects of large-scale ML pipelines and developing robust tools.
• Bridge cutting-edge research with production: integrate checkpoints, optimize evaluations, and create accessible APIs.
• Conduct experiments utilizing the latest deep-learning techniques (sparsification on 70B+ models, distributed training across thousands of GPUs).
• Design, implement, and benchmark ML algorithms; produce clear and efficient code in Python.
• Deliver prototypes that evolve into production-grade components for Le Chat and our enterprise API.
