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)