LLM Scientist, AI for Science

About this role

About Mistral 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. About the Role We are hiring an LLM Scientist to push the boundaries of AI-driven scientific discovery. You will specialize in developing and optimizing large language models (LLMs), agentic AI, and deep learning frameworks to solve open problems in the natural sciences: Biology, Chemistry, Physics, and/or Materials Science.

Your work will empower scientists with cutting-edge AI tools and accelerate research through: Model-centric innovation : Improving LLMs’ capabilities to tackle complex scientific problems (e.g., literature synthesis, autonomous experimentation, rigorous derivations). Cross-disciplinary collaboration : Partnering with Natural sciences domain experts to translate AI advancements into real-world scientific impact. What you will do AI/ML Research : Participate in end-to-end training of LLM-based solutions, with a focus on scalability, generalization, and scientific applicability .

Data creation : Design original workflows to accumulate high quality scientific data at scale, either through synthetic or crowd sourced pipelines. Algorithm Design : Use and improve reinforcement learning, or agentic systems tailored to Natural sciences challenges (e.g., modeling resolution, material property prediction, advanced data analysis and coding). Interdisciplinary Bridge : Work closely with domain scientists to identify AI opportunities and refine models based on feedback.

Tooling & Infrastructure : Contribute to company infrastructure to improve usability for model training in scientific contexts and open-source AI tools for science for non-AI experts. Autonomous Innovation : Set a research agenda for AI in science, exploring high-risk/high-reward ideas. What We're Are Looking For AI/ML Expertise : Strong background in LLMs, deep learning, or agentic AI , with a track record of applying these to real-world problems (publications, open-source contributions, or deployed systems) – at the minimum extensive experience in computer science and ideally experience scaling synthetic data generation.