AI Scientist - Domain Expert Crashworthiness

Academic Key; Paris; Hybrid; EUR 90,000 - 150,000; Full time

Job summary

Mistral AI in Paris is seeking an AI Scientist specialized in crashworthiness to develop AI-accelerated simulation capabilities for industrial engineering. You will work at the intersection of physics-based models and machine learning to enable engineering decision-making.

You will collaborate with research, product, and customer teams to translate complex structural dynamics problems into scalable data-driven solutions, with emphasis on explicit dynamics and FEM workflows.

Qualifications

• Master's degree or equivalent with strong emphasis on crashworthiness or structural dynamics.
• Hands-on experience with explicit dynamics and nonlinear FEM methods.
• Experience in simulation validation, model quality, and KPI definition.

Responsibilities

• Design and execute crashworthiness simulations and data-generation campaigns.
• Develop AI-accelerated simulation workflows and pipelines.
• Collaborate with research, product, and customer-facing teams to translate needs into data-driven models.
• Evaluate model outputs against engineering KPIs and field requirements.
• Document workflows and contribute to code quality and automation.

Skills

Crashworthiness

Solid mechanics

FEM

Python

Linux HPC

Git & tooling

Education

Master's degree in mechanical engineering or related field

Tools

Abaqus

LS-DYNA

Ansys Mechanical

Radioss

Job description

# AI Scientist - Domain Expert Crashworthiness**Mistral.ai** · ParisAbroad Paris◷ Full Time✓ Link validatedAbout 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. About the job Mistral AI is looking for a Domain Expert in crashworthiness simulations to help build AI-accelerated simulation capabilities for industrial engineering. You will work at the intersection of industrial simulation, physics modeling, machine learning, and engineering workflows. Your role is to bring deep solid-mechanics expertise into the design, training, evaluation, and deployment of AI Physics Models for real engineering use cases. This is a hands-on technical role. We are looking for someone who understands industrial structural simulation not only conceptually, but through direct experience with simulation models, solver workflows, data generation, validation, and engineering decision-making. You will work with research, product, and customer-facing teams to ensure that our models are useful against real engineering standards — not only benchmark metrics. Relevant application areas may include automotive crashworthiness, aerospace structures, consumer-electronics drop/reliability, manufacturing, durability/fatigue, and other nonlinear structural mechanics problems. What you will do Work with our research team to define, generate, and iteratively improve simulation datasets for training and evaluating crashworthiness foundation models, balancing coverage of relevant physical scenarios, simulation fidelity, and computational cost. Design and run high-fidelity simulation campaigns using structural mechanics solvers such as Abaqus, LS-DYNA, Ansys Mechanical, Radioss or equivalent tools. Define the relevant variation space for training data: geometry, mesh resolution, material behavior, boundary conditions, loading, contact, joints, failure modes, and engineering KPIs. Build or guide automated pipelines for simulation setup, execution, post-processing, dataset creation, and model evaluation. Work with research teams to train and evaluate AI models on simulation data, and diagnose failure modes caused by data gaps, poor coverage, numerical artifacts, or model limitations. Evaluate model outputs against industrial engineering needs, including field-level accuracy, scalar KPIs, deformation modes, load paths, energy absorption, stress/strain fields, failure indicators, uncertainty, and out-of-domain behavior. Work with industrial customers to understand their simulation workflows and engineering priorities, define use cases and success criteria, and incorporate their feedback into model development and validation. About you You have deep expertise in crashworthiness, solid mechanics, and structural mechanics, with substantial experience in industrial simulation workflows. You have a Master’s degree or equivalent technical depth in mechanical engineering, aerospace engineering, civil/structural engineering, computational mechanics, applied physics, or a related field. You have 4+ years of relevant industrial experience (or PhD +1 years) in domains such as automotive, aerospace, and consumer electronics. You have hands-on experience with explicit dynamics for crash or impact simulation and understand nonlinear FEM and related topics such as contact, plasticity, structural dynamics, buckling, material modeling, fracture/damage, fatigue, crashworthiness, or durability. You have direct experience with simulation validation, correlation, model quality, numerical sensitivity, and engineering KPI definition. You are a strong Python developer who can build and maintain reliable tools for simulation automation, data processing, and model evaluation. You apply sound software engineering practices, including version control with Git, automated testing, code review, and clear documentation. You have hands-on experience working in Linux and HPC environments, including submitting and monitoring batch jobs, selecting appropriate compute resources, and troubleshooting simulation workflows. You can run simulation campaigns efficiently across a compute cluster. You are comfortable operating in ambiguous technical environments and turning poorly defined industrial problems into scoped datasets, experiments, metrics, and execution plans. You communicate clearly with both deep technical experts and non-specialist stakeholders. It would be great if you Have experience applying machine learning, surrogate modeling, reduced-order modeling, optimization, or data-