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
Collaborate closely with the collaborate 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

Fluent English with excellent communication skills
able to explain technical simulation concepts to both engineering and non-technical audiences
Have a PhD in physics or engineering and 5 years+ of industry experience in a relevant domain. You work in a key engineering industry: Automotive, Aerospace or Semiconductors and have an interest in machine learning.
Self-directed
you don't need detailed roadmaps to make progress
Low-ego, collaborative, and eager to learn at the intersection of simulation and ML
Demonstrated success through industrial projects, academic work, or personal projects It would be great if you
Have a deep passion for machine learning
Have industrial or academic experience with simulation solvers (e.g. OpenFOAM, ANSYS, COMSOL, Abaqus, or equivalent)
Have applied ML methods to simulation or surrogate modelling
Have experience automating large-scale simulation campaigns on HPC clusters
Have contributed to a large open-source or industry codebase
Have publications in engineering or ML venues (NeurIPS, ICLR, etc.)
Love improving existing code by fixing typing issues, adding tests and improving CI pipelines

Applied Scientist, AI4Engineering

Key skills