AI Scientist - Physics Models

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.

Mistral is looking for AI Scientists with deep expertise in engineering sciences and machine learning to push the frontier of AI-accelerated simulation. Within AI4Engineering Science, you will research and train foundational physics models which are substantially more capable than what exists today and can be fine-tuned for downstream applications by both customers and internal teams.

You will work across the full research stack: curating high-fidelity simulation datasets, designing and training novel model architectures, and rigorously evaluating them against real engineering validation standards.

Working closely with the broader research organization, you'll ensure the foundation models you build are general enough to become the backbone of many downstream products, not just a single point solution.

This role builds on a strong, world-class foundation, and the goal is to take it further. You'll work one vertical at a time toward foundation models that genuinely transfer and fine-tune across engineering tasks, with high-quality simulation data pipelines, physics-based evaluation, and uncertainty / out-of-distribution estimation as first-class concerns. There's no inherited playbook for most of what's left to do: you'll help define the architectures, training strategies, and validation standards the team builds on, not just extend an existing one.