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).
Responsibilities
- 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 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
Requirements
- 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
Nice to Have
- 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
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