Applied Scientist / Domain Expert, AI4Engineering - EMEA

Mistral.ai; München; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Mistral.ai in Munich is seeking an experienced research engineer to design and run large-scale simulation campaigns, train AI models on physics data, and build tooling for automated dataset creation, simulation pipeline management, and model evaluation. You will collaborate with the science/research team, manage research projects, and communicate progress to engineering teams and clients.

A PhD in physics or engineering with 5+ years of industry experience is preferred, with interest in ML and

Qualifikationen

• PhD in physics or engineering required.
• 5+ years of industry experience in a relevant domain.
• Interest in machine learning and simulation at scale.

Aufgaben

• Design and run large-scale simulation campaigns.
• Train AI models on physics data and evaluate coverage, accuracy and quality.
• Build tools for automated dataset creation and simulation pipeline management.
• Collaborate with science/research team and manage research projects with engineering teams.

Kenntnisse

Fluent English

Strong communication

Team collaboration

Ausbildung

PhD in physics or engineering

Jobbeschreibung

About Mistral

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems—across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector—co-creating customized AI systems that they can run on their terms.

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 Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

What you will do

• 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
• 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

About you

• Fluent English with excellent communication skills - able to explain technical simulation concepts to both engineering and non-technical audiences
• Have a PhD in physics or engineering and 5 years+ of industry experience in a relevant domain. You work in a key engineering industry: Automotive, Aerospace or Semiconductors and have an interest in machine learning.
• 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

It would be great if you

• Have a deep passion for machine learning
• 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

What We Offer

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page.

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