About The Job
Mistral AI is looking for Applied Scientists with deep expertise in engineering sciences to work at the frontier of AI-accelerated simulation. You will work with industrial customers and internal research teams to build and deploy AI Physics Models alongside our existing offerings of Large Language Models (LLMs).
You will contribute across the full stack: curating high-fidelity simulation datasets, training and evaluating models, and delivering production-grade AI solutions directly to engineering teams. Target domains include computational fluid dynamics, structural mechanics, semiconductor design, multi-physics modelling, and digital twins.
Working cross-functionally with research, product, and customer-facing teams, you will ensure our models meet real engineering standards — not just benchmark metrics.
What you will do
- Design and run large-scale simulation campaigns using domain-specific solvers (e.g. OpenFOAM, ANSYS, COMSOL, Abaqus)
- Run training of AI models on physics data, with rigorous evaluation of coverage, accuracy, and quality against industry validation standards
- Build tools and frameworks for automated dataset creation, simulation pipeline management, and model evaluation
- Develop agents and RAG that integrate LLMs with engineering simulation workflows
- Collaborate closely with the collaborate with the science/research team on training runs and diagnose failure modes arising from data gaps or architecture limitations
- Manage research projects and client communications with engineering teams
About you
- Fluent English with excellent communication skills - able to explain technical simulation concepts to both engineering and non-technical audiences
- PhD or Master's in AI or an engineering science: Mechanical Engineering, Electrical Engineering, Computational Fluid Dynamics, Structural Mechanics, Semiconductor Engineering, or a related field. A solid understanding of deep learning and engineering or physics is a must.
- Comfortable with PyTorch or JAX for implementing and training models
- You write clean, readable Python code and are comfortable in Linux/HPC environments
- Self-directed - you don't need detailed roadmaps to make progress
- Low-ego, collaborative, and eager to learn at the intersection of simulation and ML
- Demonstrated success through industrial projects, academic work, or personal projects
It would be great if you
- Have industrial or academic experience with simulation solvers (e.g. OpenFOAM, ANSYS, COMSOL, Abaqus, or equivalent)
- Have applied ML methods to simulation or surrogate modelling
- Have experience automating large-scale simulation campaigns on HPC clusters
- Have contributed to a large open-source or industry codebase
- Have publications in engineering or ML venues (NeurIPS, ICLR, etc.)
- Love improving existing code by fixing typing issues, adding tests and improving CI pipelines