FEM Consultant

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 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)