Mistral is looking for AI Scientists with deep expertise in engineering sciences and machine learning to push the frontier of AI-accelerated simulation. Within AI4Engineering Science, you will research and train foundational physics models which are substantially more capable than what exists today and can be fine-tuned for downstream applications by both customers and internal teams.

You will work across the full research stack: curating high-fidelity simulation datasets, designing and training novel model architectures, and rigorously evaluating them against real engineering validation standards.

Working closely with the broader research organization, you'll ensure the foundation models you build are general enough to become the backbone of many downstream products, not just a single point solution.

This role builds on a strong, world-class foundation, and the goal is to take it further. You'll work one vertical at a time toward foundation models that genuinely transfer and fine-tune across engineering tasks, with high-quality simulation data pipelines, physics-based evaluation, and uncertainty / out-of-distribution estimation as first-class concerns. There's no inherited playbook for most of what's left to do: you'll help define the architectures, training strategies, and validation standards the team builds on, not just extend an existing one.

What You Will Do

Research and train novel foundation models for physics simulation, pushing past today's state of the art in accuracy, generalization, and scale
Design and run large-scale simulation campaigns using domain-specific solvers to build the high-fidelity datasets foundational physics models need
Investigate architectures and training strategies (e.g. multi-fidelity training, pretraining objectives, scaling behavior) that let a single foundation model transfer and fine-tune well across diverse engineering tasks
Rigorously evaluate model coverage, accuracy, and robustness against industry validation standards, and diagnose failure modes arising from data gaps or architecture limitations
Stay on top of the latest developments in the scientific community and contribute to Mistral's standing at the frontier of AI-for-engineering research

Qualifications

PhD or Master's in CS/AI or an engineering science: Mechanical Engineering, Electrical Engineering, Computational Fluid Dynamics, Structural Mechanics, EDA, Semiconductor Engineering, or a related field
Strong, hands-on machine learning expertise with a deep understanding of model architectures, training dynamics, and evaluation methodology is core to this role
You have developed ML methods for simulation or surrogate modelling
You write clean, readable Python code and are comfortable in Linux/HPC environments
Fluent English with excellent communication skills, able to explain technical simulation and ML concepts to both engineering and non-technical audiences
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, LS-DYNA, ANSYS, COMSOL, Abaqus, Fluent, STAR-CCM+, PowerFlow, NekRS, Tau/CODA, JAX-Fluids, or equivalent; Cadence/Synopsys/Siemens EDA or equivalent)
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 (AIAA, ASME, JFM, NeurIPS, ICLR, etc.)