Applied Scientist, AI4Engineering

Responsibilities

• Design and run large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
• Train 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 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.

Requirements

• Fluent English with excellent communication skills — able to explain technical simulation concepts to both engineering and non-technical audiences.
• 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.
• Have a deep passion for machine learning.
• Experience with simulation solvers (OpenFOAM, ANSYS, COMSOL, Abaqus, or equivalent).
• Applied ML methods to simulation or surrogate modelling.
• Experience automating large-scale simulation campaigns on HPC clusters.
• Contributed to a large open-source or industry codebase.
• Publications in engineering or ML venues (NeurIPS, ICLR, etc.).
• Love improving existing code by fixing typing issues, adding tests, and improving CI pipelines.