Applied Scientist: AI for Engineering Simulations

Mistral; Amsterdam; On-site; EUR 120,000 - 160,000; Full time

Job summary

Mistral AI is seeking Applied Scientists to work at the frontier of AI-accelerated simulation across industries. You will create and deploy AI Physics Models, curate high-fidelity datasets, and train models to production quality for engineering teams. Domains include CFD, structural mechanics, semiconductor design, and digital twins.

Join a cross-functional team to ensure models meet real engineering standards, not just benchmarks, while advancing simulation and ML at scale.

Qualifications

• PhD or Master's in AI or an engineering science with strong modelling background.
• Experience applying ML to physics-based simulation and engineering problems.
• Proven ability to communicate complex concepts to diverse audiences.

Responsibilities

• Design and run large-scale simulation campaigns using domain-specific solvers.
• Train AI models on physics data and evaluate coverage and accuracy.
• Build tools for automated dataset creation, simulation pipelines, and model evaluation.
• Develop agents and RAG that integrate LLMs with simulation workflows.
• Collaborate with science/research teams on training runs and diagnose failures.
• Manage research projects and client communications with engineering teams.

Skills

Fluent English communication

PhD or MSc in AI/Engineering

PyTorch / JAX

Python

Linux / HPC

Self-directed work style

Team collaboration

Education

PhD in AI or Engineering

Master’s in AI, ML, or related field

Tools

OpenFOAM

ANSYS

COMSOL

Abaqus

PyTorch

JAX

Job description

Mistral AI is seeking Applied Scientists to work at the frontier of AI-accelerated simulation across industries. You will create and deploy AI Physics Models, curate high-fidelity datasets, and train models to production quality for engineering teams. Domains include CFD, structural mechanics, semiconductor design, and digital twins.

Join a cross-functional team to ensure models meet real engineering standards, not just benchmarks, while advancing simulation and ML at scale.