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'll DoSet up and run FEM simulations across structural, thermal, or fluid domains, producing clean reference datasets for training AI-powered solversDefine and validate meshing strategies, boundary conditions, and solver configurations aligned with real-world engineering scenariosBenchmark our neural solver outputs against traditional FEM results, identifying discrepancies and edge casesAutomate simulation workflows (parametric sweeps, batch runs, post-processing) through scriptingCollaborate asynchronously with our research and engineering teams: align on simulation requirements, iterate on results, and enhance data quality based on model performanceProactively identify and resolve failure modes in simulation setups (mesh sensitivity, convergence issues, unrealistic boundary conditions, numerical artifacts) with pragmatic engineering solutionsTools & StackProficiency in at least one major FEM suite: ANSYS, Abaqus, COMSOL, OpenFOAM, Code_Aster, or similarScripting and automation skills: Python, APDL, or equivalentPost-processing and data management: ParaView, NumPy, or similarWhat We're Looking ForStrong 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 methodsExperience in at least one domain: structural mechanics, heat transfer, fluid-structure interaction, or CFDAbility to manage multiple simulation campaigns and shift priorities without compromising accuracy or qualityClear and consistent communication skills in a fully remote, async-first team environmentNice to HaveExperience with parametric studies or design of experiments for simulationFamiliarity with ML/AI methods in scientific computing (PINNs, neural operators, surrogate models)Background in manufacturing or industrial engineering applicationsLet Us KnowYour portfolio (past simulation projects, publications, case studies, or anything that showcases your work)