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Amsterdam
1 May 2026
Applied Scientist / Research Engineer, AI4Engineering - EMEA logo

Applied Scientist / Research Engineer, AI4Engineering - EMEA

Mistral

Amsterdam
1 May 2026
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Applied Scientist / Research Engineer, AI4Engineering - EMEA

Mistral AI is hiring an Applied Scientist / Research Engineer to work on AI-accelerated simulation for engineering domains. The role involves building and deploying AI Physics Models, collaborating with industrial customers and research teams, and contributing across the full stack. Required expertise includes deep learning, engineering sciences (e.g., CFD, structural mechanics), and Python.

HybridFull-timeEntry LevelPyTorchJAX

Applied Scientist / Research Engineer, AI4Engineering - EMEA

Mistral AI is hiring an Applied Scientist / Research Engineer to work on AI-accelerated simulation for engineering domains. The role involves building and deploying AI Physics Models, collaborating with industrial customers and research teams, and contributing across the full stack. Required expertise includes deep learning, engineering sciences (e.g., CFD, structural mechanics), and Python.

Apply
HybridFull-timeEntry LevelPyTorch

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)
PyTorchJAXOpenFOAMANSYSCOMSOLAbaqusPythonLinuxHPC
Soft Skills
communicationself-directedlow-egocollaborativeeager to learn

Preferred Qualifications

Technical (Nice-to-have)
simulation solverssimulation or surrogate modellingautomating large-scale simulation campaigns on HPC clusterslarge open-source or industry codebaseNeurIPSICLR
Tools (Nice-to-have)
CI pipelines

Key Responsibilities

  • •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
  • •Develop agents and RAG that integrate LLMs with engineering simulation workflows
  • •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
AIMachine LearningEngineering SimulationCFDDeep LearningPythonPyTorchJAXHPCHybrid
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