Applied Scientist / Research Engineer, Mistral AI

• 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

Desirable

• 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
• 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