Senior AI Solutions Architect, Industrial Engineering

What you'll do

• Support business development and sales teams to drive ecosystem success across industrial engineering accounts.
• Work directly with engineering-software developers and customer simulation teams in a customer-facing capacity.
• Help developers GPU-accelerate and scale CAE, CFD, and FEA solvers on NVIDIA HPC platforms.
• Apply physics-informed ML and surrogate modeling to compress design and simulation cycles.
• Analyze simulation and engineering application architectures to identify opportunities for performance acceleration.
• Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.
• Provide technical feedback to internal engineering, product, and research teams.

What we're looking for

• BS/MS/PhD in Mechanical, Aerospace, Civil, or Chemical Engineering, Computational Science, Applied Mathematics, Physics, or a related technical field (or equivalent experience).
• 8+ years of experience in CAE/CFD/FEA or computational engineering, including numerical simulation, solver development, or HPC-based engineering analysis.
• Hands-on experience with commercial or open-source simulation tools such as Ansys, Siemens Simcenter, Altair, COMSOL, Cadence Fidelity CFD, OpenFOAM, LS-DYNA, or Abaqus.
• Strong grounding in numerical methods (FEM/FVM/spectral), linear algebra, and the mathematics behind physics solvers.
• Experience in algorithm programming using Python and C/C++ with familiarity GPU-accelerating compute-intensive workloads.
• Familiarity with accelerated computing platforms, GPU-based distributed systems, and HPC clusters/schedulers like Slurm.
• Familiarity with containers, numerical libraries, modular software design, version control, and GitHub.
• Experience designing, prototyping, and building complex solutions for customers across data pipelines, solvers, compute, networking, and orchestration.
• Experience GPU-accelerating CFD/FEA solvers or developing physics-ML and surrogate models (preferred).
• Background with NVIDIA Omniverse, OpenUSD, and digital-twin workflows for industrial and engineering simulation (preferred).
• Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA-X math libraries (preferred).
• Experience with Kubernetes, distributed training, and large-scale inference (preferred).
• Experience supporting or using PCIe accelerators such as GPUs, FPGAs, and DSPs from evaluation to production stages (preferred).