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AI Scientist - Domain Expert Crashworthiness

Mistral AIParisScience
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Salary
Not listed
Job type
Full-time
Work mode
Remote
Posted
28 Sept 2026
Status
Open · checked 7d ago

About this role

The AI Scientist - Domain Expert Crashworthiness position at Mistral AI in Paris is an exciting opportunity for a seasoned simulation professional to shape advanced AI-driven tools for industrial engineering. This hands-on technical role is ideal for a solid mechanics expert eager to combine physics modeling with cutting-edge artificial intelligence.

Role Overview

In this full-time role based in Paris with remote options available, the successful candidate works at the crossroads of industrial simulation, machine learning, and structural engineering. The professional brings deep solid-mechanics knowledge into the design, training, evaluation, and deployment of AI physics models for real engineering use cases. Collaborating closely with research, product, and customer-facing teams, the scientist ensures that developed models align with actual industrial engineering standards rather than mere benchmark metrics.

Key Responsibilities

Collaborate with the research team to define, generate, and iteratively improve simulation datasets that train and evaluate crashworthiness foundation models.
Design and execute high-fidelity simulation campaigns utilizing structural mechanics solvers like Abaqus, LS-DYNA, Ansys Mechanical, Radioss, or equivalent applications.
Establish the variation space for training data, encompassing geometry, mesh resolution, material behavior, boundary conditions, loading, contact, joints, and failure modes.
Develop or guide automated pipelines dedicated to simulation setup, execution, post-processing, dataset creation, and model evaluation.
Partner with researchers to train and evaluate artificial intelligence models on simulation data while diagnosing failure issues caused by data gaps or numerical artifacts.
Appraise model outputs against industrial engineering needs, checking field-level accuracy, scalar KPIs, deformation modes, load paths, and stress fields.
Engage directly with industrial clients to understand their simulation workflows, determine success criteria, and translate feedback into model enhancement.

Requirements

Deep expertise in crashworthiness, solid mechanics, and structural mechanics, backed by substantial industrial simulation workflow experience.
A Master’s degree or equivalent technical depth in mechanical engineering, aerospace engineering, civil or structural engineering, computational mechanics, or applied physics.
At least 4 years of relevant industrial experience, or a PhD plus 1 year of experience, within fields such as automotive, aerospace, or consumer electronics.
Hands-on background utilizing explicit dynamics for crash or impact simulation, alongside knowledge of nonlinear FEM, contact, plasticity, structural dynamics, buckling, and fracture.
Direct proficiency in simulation validation, correlation, model quality, numerical sensitivity, and engineering KPI definition.
Strong Python development skills capable of building and maintaining reliable tooling for simulation automation, data processing, and evaluation using version control.
Practical experience operating in Linux and HPC environments, including batch job submission, resource selection, and cluster-wide simulation management.
Comfort operating in ambiguous technical environments by turning vague industrial problems into scoped datasets, experiments, and execution plans.

Nice to Have

Background applying machine learning, surrogate modeling, reduced-order modeling, optimization, or data-driven methods to simulation tasks.
Contributions to reusable internal tools, open-source code, simulation automation frameworks, or production-quality engineering workflows.
Prior experience collaborating directly with industrial customers, product teams, or engineering decision-makers.
Published works, patents, internal technical leadership, or recognized contributions within engineering, simulation, computational mechanics, or ML-for-physics communities.

Skills and Tools

Python
Explicit dynamics and nonlinear FEM tools (such as Abaqus, LS-DYNA, Ansys Mechanical, Radioss)
Linux and HPC cluster environments
Version control using Git

About the Company

Mistral AI provides comprehensive, full-stack artificial intelligence solutions ranging from frontier models to developer tools, applications, and compute power. The company partners with enterprises across challenging, high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector to co-create customized AI systems. Operating with a dynamic, collaborative, and low-ego culture, the distributed workforce spans Europe, North America, Asia, and the Middle East.

How to Apply

Interested candidates should visit the official company careers page on mistral.ai to submit their application for this role.

Skills and tech stack

PythonMachine Learning

Questions candidates ask

Where is this job located?

This position is based in Paris, with remote options available as part of a globally distributed team framework.

What level of experience is required for the AI Scientist role?

Applicants must have at least 4 years of relevant industrial experience, or a PhD combined with 1 year of relevant experience.

What educational background is expected?

Candidates need a Master’s degree or equivalent technical depth in mechanical engineering, aerospace engineering, civil or structural engineering, computational mechanics, applied physics, or a related field.

What programming language is required?

Proficiency in Python is required for building and maintaining reliable tools for simulation automation, data processing, and model evaluation.

What software tools and environments will I use?

You will use structural mechanics solvers such as Abaqus, LS-DYNA, Ansys Mechanical, or Radioss, alongside Linux and HPC environments for running batch jobs.

Ready to apply?

The application is on the official Mistral AI careers page.

Apply on company site