LLM Scientist, AI for Science

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.
• State-of-the-art Knowledge: Familiarity with the latest advancements in LLM architectures and post training.
• Technical Depth: Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques (e.g., fine-tuning, RLHF).
• Scientific Capability: Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge) to design effective AI solutions.
• Engineering Rigor: Proficiency in scientific computing (e.g., numerical methods, HPC) and software best practices.
• Collaboration: Experience translating technical AI concepts for non-experts (e.g., scientists, engineers) and iterating based on user needs.
• Mindset: Low ego, team player, and eager to tackle operational challenges (e.g., data pipelines, model deployment).

Motivational paragraph

When applying for the AI for Science team at Mistral, please describe in a short paragraph a potential project that you would like to contribute to the team, why it is challenging and interesting in general, and more specifically for Mistral. Please also highlight the specific skill set that you can bring to the team.

What We Offer

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.