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PhysicsX is a physics AI company for industrials, building a new simulation software stack to deliver deep physics AI enablement across the engineering lifecycle. The company partners with leading organizations in aerospace & defense, automotive, semiconductors, materials, and energy & renewables.
You will work closely with Data Scientists, Machine Learning Engineers, and customers to understand and define engineering challenges. Your responsibilities include:
• Own the technical delivery of building complex structural and multi-physics models from geometry clean-up and meshing, through simulation and post-processing of complex real-world phenomena, integrating experimental data for model validation.
• Partner with customers to address their most complex engineering challenges through advanced FEA & AI solutions; communicate results clearly.
• Work at the intersection of CAE and Data Science to generate high-quality simulation datasets for training Machine/Deep Learning models, leveraging data sampling techniques to efficiently capture the design space, reduce computational cost, and enhance model accuracy.
• Accelerate high-fidelity modelling using Flux (the company's cloud platform) and on-premise HPC resources.
• Document work through clear technical reports and process notes, contributing to the team's shared knowledge base.
• Travel domestically and globally (North America, Europe, Asia, Oceania) up to 2–3 weeks per quarter to work side-by-side with customers on-site.
The role is based in NYC, working 2–3 days per week in the central office.
REQUIREMENTS:
• 2+ years of industry experience (post Master's or PhD) delivering FEA, MBS, or structural dynamics analysis in a commercial engineering environment.
• Hands-on expertise in structural dynamics and vibration with ability to apply fundamental knowledge to real-world phenomena across a wide range of engineering applications.
• Highly proficient in at least one FEA solver (Nastran, Abaqus, or ANSYS) and at least one MBS solver (e.g., Adams, SIMPACK, Virtual Dynamics, or Excite M).
• Working knowledge of the surrounding pre/post tool chain (SimLab, HyperMesh, OptiStruct, Hypergraph or equivalents).
• Proficiency in Python for pipeline scripting and automation.
• Comfortable owning a workflow end to end: model build, solver setup, run management, and results interpretation, with ability to make engineering judgment calls independently.
• Advantageous: Proficiency in parametric CAD modelling (NX or CATIA) and exposure to open source tools (Calculix, OpenRadioss).
