<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <strong>About Mistral<br/><br/></strong>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.<br/><br/>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.<br/><br/>We are a dynamic, collaborative team passionate about AI and its potential to transform society.<br/><br/>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.<br/><br/>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.<br/><br/><strong>About The Job<br/><br/></strong>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).<br/><br/>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.<br/><br/>Working cross-functionally with research, product, and customer-facing teams, you will ensure our models meet real engineering standards — not just benchmark metrics.<br/><br/><strong>What You Will Do<br/><br/></strong><ul><li> Design and run large-scale simulation campaigns using domain-specific solvers (e.g. OpenFOAM, ANSYS, COMSOL, Abaqus)</li><li> Run training of AI models on physics data, with rigorous evaluation of coverage, accuracy, and quality against industry validation standards</li><li> Build tools and frameworks for automated dataset creation, simulation pipeline management, and model evaluation</li><li> Develop agents and RAG that integrate LLMs with engineering simulation workflows</li><li> Collaborate closely with the collaborate with the science/research team on training runs and diagnose failure modes arising from data gaps or architecture limitations</li><li> Manage research projects and client communications with engineering teams<br/><br/><br/></li></ul><strong>About You<br/><br/></strong><ul><li> Fluent English with excellent communication skills - able to explain technical simulation concepts to both engineering and non-technical audiences</li><li> 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</li><li> Comfortable with PyTorch or JAX for implementing and training models</li><li> You write clean, readable Python code and are comfortable in Linux/HPC environments</li><li> Self-directed - you don't need detailed roadmaps to make progress</li><li> Low-ego, collaborative, and eager to learn at the intersection of simulation and ML</li><li> Demonstrated success through industrial projects, academic work, or personal projects<br/><br/><br/></li></ul><strong>It would be great if you<br/><br/></strong><ul><li> Have industrial or academic experience with simulation solvers (e.g. OpenFOAM, ANSYS, COMSOL, Abaqus, or equivalent)</li><li> Have applied ML methods to simulation or surrogate modelling</li><li> Have experience automating large-scale simulation campaigns on HPC clusters</li><li> Have contributed to a large open-source or industry codebase</li><li> Have publications in engineering or ML venues (NeurIPS, ICLR, etc.)</li><li> Love improving existing code by fixing typing issues, adding tests and improving CI pipelines<br/><br/><br/></li></ul><strong>Benefits<br/><br/></strong><strong>Locations:</strong> Munich, Paris, London, Amsterdam, Lausanne, Linz. Hybrid work model.<br/><br/><strong>France<br/><br/></strong><ul><li> Competitive cash salary and equity</li><li> Daily lunch vouchers</li><li> Monthly contribution to a Gympass subscription</li><li> Monthly contribution to a mobility pass</li><li> Full health insurance for you and your family</li><li> Generous parental leave policy</li><li> Visa sponsorship<br/><br/><br/></li></ul><strong>UK<br/><br/></strong><ul><li> Competitive cash salary and equity</li><li> Health insurance</li><li> Transportation reimbursement (office parking or £90/month public transport)</li><li> £90/month gym membership reimbursement</li><li> £200/month meal allowance</li><li> Pension plan: SmartPension (5% Employee &amp; 3% Employer)<br/><br/><br/></li></ul>By applying, you agree to our Applicant Privacy Policy. </div>