This role pays more than
71%
of similar roles. Most pay
$162,200–$246,150
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$204k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior AI Solutions Architect - Industrial Engineering, you will join the Solutions Architecture team to support industrial engineering accounts including CAE, CFD, and FEA software vendors and industrial OEMs. You will serve as a technical advisor to developers, embedding accelerated computing, Omniverse, and physics-ML into solver, simulation, and digital-twin pipelines. Your daily work involves helping customers GPU-accelerate solvers, applying surrogate modeling via PhysicsNeMo or Modulus, and delivering technical demonstrations. The role requires expertise in numerical methods like FEM and FVM, along with proficiency in Python, C/C++, CUDA, and HPC clusters using Slurm. You will address the challenge of accelerating design cycles for physics-based engineering workflows. Required skills include experience with tools like OpenFOAM or Ansys, as well as knowledge of containerization, version control, and large-scale inference to solve complex simulation problems.
Act as a technical advisor to simulation and engineering software developers to integrate NVIDIA technologies into their workflows.
Help developers GPU-accelerate and scale CAE, CFD, and FEA solvers on NVIDIA accelerated computing and HPC platforms.
Implement physics-informed machine learning and surrogate modeling to compress design and simulation cycles.
Integrate NVIDIA Omniverse and digital twin technologies into industrial engineering pipelines.
Analyze application architectures to identify specific opportunities for performance acceleration.
Deliver technical demonstrations, trainings, and hackathons regarding NVIDIA solutions and platforms.
Partner with sales and business development teams to drive success across Industrial Engineering accounts.
Provide technical feedback to internal engineering, product, and research teams based on customer needs.
What we're looking for
BS/MS/PhD in Mechanical, Aerospace, Civil, 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 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.
Proficiency in Python and C/C++ for algorithm programming with familiarity in GPU-accelerating compute-intensive workloads.
Familiarity with accelerated computing platforms, GPU-based distributed systems, HPC clusters, Slurm, containers, and version control.
Experience designing and building complex solutions involving data pipelines, solvers, compute, networking, and orchestration.
Experience with CUDA/CUDA-X libraries, NVIDIA Omniverse, OpenUSD, or physics-ML models (preferred).
AI
Machine Learning
Deep Learning
Robotics
GPU
HPC
Docker
Kubernetes
PyTorch
TensorFlow
Isaac Sim
Isaac Lab
Nsight Systems
Transformers
VLM
VLA
Synthetic Data Generation
Digital Twins
Parallel Computing
AI
Machine Learning
Deep Learning
Robotics
GPU
HPC
Docker
Kubernetes
PyTorch
TensorFlow
Vision Transformer
VLM
VLA
Nsight Systems
Synthetic Data Generation
Digital Twins