We're seeking a FEM Consultant to help us generate, validate, and refine high-fidelity simulation datasets that power our neural solvers. You will work on real-world engineering simulationsstructural, thermal, and fluidemphasizing accuracy, reproducibility, and practical relevance to industrial use cases rather than academic exercises.
What you will do
- Set up and run FEM simulations across structural, thermal, or fluid domains, producing clean reference datasets for training AI-powered solvers
- Define and validate meshing strategies, boundary conditions, and solver configurations aligned with real-world engineering scenarios
- Benchmark our neural solver outputs against traditional FEM results, identifying discrepancies and edge cases
- Automate simulation workflows (parametric sweeps, batch runs, post-processing) through scripting
- Collaborate asynchronously with our research and engineering teams: align on simulation requirements, iterate on results, and enhance data quality based on model performance
- Proactively identify and resolve failure modes in simulation setups (mesh sensitivity, convergence issues, unrealistic boundary conditions, numerical artifacts) with pragmatic engineering solutions
Tools & Stacks
- Proficiency in at least one major FEM suite: ANSYS, Abaqus, COMSOL, OpenFOAM, Code_Aster, or similar
- Scripting and automation skills: Python, APDL, or equivalent
- Post-processing and data management: ParaView, NumPy, or similar
What we are looking for
- Strong fundamentals and hands-on experience in FEM (5+ years in consulting, R&D, or engineering)
- In-depth knowledge of continuum mechanics, material modeling, and numerical methods
- Experience in at least one domain: structural mechanics, heat transfer, fluid-structure interaction, or CFD
- Ability to manage multiple simulation campaigns and shift priorities without compromising accuracy or quality
- Clear and consistent communication skills in a fully remote, async-first team environment
Nice to have
- Experience with parametric studies or design of experiments for simulation
- Familiarity with ML/AI methods in scientific computing (PINNs, neural operators, surrogate models)
- Background in manufacturing or industrial engineering applications
Let us know
- Your portfolio (past simulation projects, publications, case studies, or anything that showcases your work)
