- Employment
- Contract
About the role
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 simulations—structural, thermal, and fluid—emphasizing accuracy, reproducibility, and practical relevance to industrial use cases rather than academic exercises.
What You'll 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 & Stack
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're 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)
Perks & benefits
- Async
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