Mistral27d ago

Applied Scientist / Research Engineer, AI4Engineering - EMEA

France·ParisFull-timemid

Data ScientistData

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Quick Summary

Key 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…

Requirements Summary

• 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,…

Technical Tools

pythonpytorchci-cddeep-learninglinux

At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.

We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.

We are a dynamic, collaborative team passionate about AI and its potential to transform society.

Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.

Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers.

About the Role

~1 min read

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.

Responsibilities

~1 min read

• 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

• 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

• 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