Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are looking for a Senior Solutions Architect to support our Industrial Engineering accounts — the CAE, CFD, and FEA software vendors, engineering-simulation platforms, and industrial OEMs building the next generation of physics-based and AI-augmented engineering workflows on NVIDIA platforms. In this role you will be a trusted technical advisor to simulation and engineering software developers, embedding NVIDIA accelerated computing, Omniverse, and physics-ML into solver, simulation, and digital-twin pipelines. You will play a direct role in improving application performance, accelerating design and simulation cycles, and establishing the technical foundation required for next-generation engineering and digital-twin systems. What you’ll be doing: • Support Business Development and Sales teams as part of a small Solutions Architecture team, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive ecosystem success across Industrial Engineering accounts (CAE/CFD/FEA ISVs, simulation platforms, and industrial OEMs). • Work directly with engineering-software developers and customer simulation teams in a customer-facing setting. • Help developers GPU-accelerate and scale CAE/CFD/FEA solvers and structural, thermal, and fluid-dynamics workloads on NVIDIA accelerated computing and HPC platforms. • Apply physics-informed ML and surrogate modeling (e.g., NVIDIA PhysicsNeMo / Modulus) and NVIDIA Omniverse digital twins to compress design, simulation, and optimization cycles. • Analyze simulation and engineering application architectures and find opportunities for acceleration. • Provide feedback and collaborate with engineering, product, and research teams. • Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms. What we need to see: • 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 working in CAE/CFD/FEA or computational engineering — numerical simulation, solver development, or HPC-based engineering analysis. • Hands-on experience with commercial or open-source simulation tools (e.g., Ansys, Siemens Simcenter, Altair, COMSOL, Cadence Fidelity CFD, OpenFOAM, LS-DYNA, Abaqus). • Strong grounding in numerical methods (FEM/FVM/spectral), linear algebra, and the mathematics behind physics solvers. • Experience in algorithm programming using languages like Python and C/C++, with familiarity GPU-accelerating compute-intensive workloads. • Familiarity with accelerated computing platforms, GPU-based distributed systems, and HPC clusters/schedulers (e.g., Slurm). • Familiarity with containers, numerical libraries, modular software design, version control, GitHub. • Experience designing, prototyping, and building complex solutions for customers; able to reason across components such as data pipelines, solvers, compute, networking, and orchestration. • Solid written and oral communication skills and familiarity with collaborative environments. • Team player who can learn, react, and adapt quickly, with an attitude to work in a fast-paced environment. Ways to stand out from the crowd: • Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks). • Background with NVIDIA Omniverse, OpenUSD, and digital-twin workflows for industrial and engineering simulation. • Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA-X math libraries (cuBLAS, cuSPARSE, cuDNN). • Experience with Kubernetes, distributed training, and large-scale inference. • Experience supporting or using PCIe accelerators such as GPUs, FPGAs, DSPs from evaluation to production stages. NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking individuals in the world working for us. If you're creative and autonomous, we want to hear from you! #NALASAHiring Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 14, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Work directly with engineering-software developers and customer simulation teams in a customer-facing setting.
Help developers GPU-accelerate and scale CAE/CFD/FEA solvers and structural, thermal, and fluid-dynamics workloads on NVIDIA accelerated computing and HPC platforms.
Apply physics-informed ML and surrogate modeling (e.g., NVIDIA PhysicsNeMo / Modulus) and NVIDIA Omniverse digital twins to compress design, simulation, and optimization cycles.
Analyze simulation and engineering application architectures and find opportunities for acceleration.
Provide feedback and collaborate with engineering, product, and research teams.
Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.
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 working in CAE/CFD/FEA or computational engineering — numerical simulation, solver development, or HPC-based engineering analysis.
Hands-on experience with commercial or open-source simulation tools (e.g., Ansys, Siemens Simcenter, Altair, COMSOL, Cadence Fidelity CFD, OpenFOAM, LS-DYNA, Abaqus).
Strong grounding in numerical methods (FEM/FVM/spectral), linear algebra, and the mathematics behind physics solvers.
Experience in algorithm programming using languages like Python and C/C++, with familiarity GPU-accelerating compute-intensive workloads.
Familiarity with accelerated computing platforms, GPU-based distributed systems, and HPC clusters/schedulers (e.g., Slurm).
Familiarity with containers, numerical libraries, modular software design, version control, GitHub.
Experience designing, prototyping, and building complex solutions for customers; able to reason across components such as data pipelines, solvers, compute, networking, and orchestration.
Solid written and oral communication skills and familiarity with collaborative environments.
Team player who can learn, react, and adapt quickly, with an attitude to work in a fast-paced environment.
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks).
Background with NVIDIA Omniverse, OpenUSD, and digital-twin workflows for industrial and engineering simulation.
Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA-X math libraries (cuBLAS, cuSPARSE, cuDNN).
Experience with Kubernetes, distributed training, and large-scale inference.
Experience supporting or using PCIe accelerators such as GPUs, FPGAs, DSPs from evaluation to production stages.
#NALASAHiring
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 14, 2026.

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Platform: eightfold
Effort: medium

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