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Mistral AI is seeking a Domain Expert in Crashworthiness Simulations to build AI-accelerated simulation capabilities for industrial engineering. You will work at the intersection of industrial simulation, physics modeling, machine learning, and engineering workflows, bringing deep solid-mechanics expertise to the design, training, evaluation, and deployment of AI Physics Models for real engineering use cases.
This is a hands-on technical role requiring direct experience with simulation models, solver workflows, data generation, validation, and engineering decision-making. You will partner with research, product, and customer-facing teams to ensure models meet real engineering standards, not just benchmark metrics. Application areas include automotive crashworthiness, aerospace structures, consumer-electronics reliability, manufacturing, durability/fatigue, and nonlinear structural mechanics.
Key responsibilities:
- Work with research teams to define, generate, and iteratively improve simulation datasets for training and evaluating crashworthiness foundation models, balancing physical scenario coverage, simulation fidelity, and computational cost.
- Design and run high-fidelity simulation campaigns using structural mechanics solvers (Abaqus, LS-DYNA, Ansys Mechanical, Radioss, or equivalent).
- Define 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.
- Train and evaluate AI models on simulation data; diagnose failure modes from data gaps, poor coverage, numerical artifacts, or model limitations.
- Evaluate model outputs against industrial engineering needs: 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 simulation workflows and engineering priorities, define use cases and success criteria, and incorporate feedback into model development and validation.
Requirements:
- Deep expertise in crashworthiness, solid mechanics, and structural mechanics with substantial industrial simulation workflow experience.
- Master's degree or equivalent technical depth in mechanical engineering, aerospace engineering, civil/structural engineering, computational mechanics, applied physics, or related field.
- 4+ years of relevant industrial experience (or PhD + 1 year) in automotive, aerospace, or consumer electronics.
- Hands-on experience with explicit dynamics for crash/impact simulation; understanding of nonlinear FEM, contact, plasticity, structural dynamics, buckling, material modeling, fracture/damage, fatigue, crashworthiness, or durability.
- Direct experience with simulation validation, correlation, model quality, numerical sensitivity, and engineering KPI definition.
- Strong Python developer capable of building and maintaining reliable tools for simulation automation, data processing, and model evaluation; proficiency with Git, automated testing, code review, and documentation.
- Hands-on experience in Linux and HPC environments: submitting/monitoring batch jobs, selecting compute resources, troubleshooting simulation workflows, running campaigns across compute clusters.
- Comfort operating in ambiguous technical environments, turning poorly defined industrial problems into scoped datasets, experiments, metrics, and execution plans.
- Clear communication with both deep technical experts and non-specialist stakeholders.
Desirable:
- Experience applying machine learning, surrogate modeling, reduced-order modeling, optimization, or data-driven methods to simulation problems.
- Contributions to reusable internal tools, open-source code, simulation automation frameworks, or production-quality engineering workflows.
- Direct experience with industrial customers, product teams, or engineering decision-makers.
- Publications, patents, internal technical leadership, or recognized contributions in engineering, simulation, computational mechanics, or ML-for-physics communities.
