Industrial simulation depends on turning product and device geometry into valid, efficient discretizations. This role will advance computational geometry, meshing, simulation-ready representations, and AI-native algorithms for NVIDIA GPU platforms.
We are looking for an applied researcher who can build methods that move design data reliably from CAD to simulation. You will develop geometry, meshing, and discretization algorithms that improve robustness, numerical accuracy, and end-to-end performance across CAE, EDA, semiconductor, and scientific-computing workflows. Join us in reinventing the geometric foundation of engineering simulation!
What you'll be doing:
Build algorithms for computational geometry, computer-aided engineering and design interoperability, mesh generation, mesh adaptation, spatial data structures, and curved discretization.
Investigate differentiable geometry, AI-native geometry processing, learning-based meshing and discretization, and design-to-simulation workflows for inverse design, simulation-ready digital twins, and autonomous engineering workflows.
Explore solver- and hardware-aware geometry and discretization methods that jointly optimize mesh quality, numerical accuracy, robustness, and comprehensive simulation efficiency.
Define benchmarks for mesh quality, geometry conversion, discretization accuracy, robustness, downstream solver impact, and end-to-end simulation performance.
Collaborate with teams across Omniverse, OpenUSD, Warp, solver engineering, NVIDIA Research, universities, and industrial partners involved in computer-aided engineering, electronic design automation, chip manufacturing, electronics, and digital twin workflows.
What we need to see:
PhD or equivalent experience in computer science, computational geometry, scientific computing, graphics, applied mathematics, computational mechanics, engineering, or a related field.
5+ years of experience.
Background in computational geometry, geometry processing, mesh generation, adaptive discretization, CAD and CAE algorithms, or simulation-ready representations with 5+ years proven experience working in computational engineering.
C++ and Python skills, with experience building algorithms for sophisticated geometry.
Understanding of boundary representations, topology, mesh quality, discretization error, numerical robustness, and solver requirements, supported by research, software, or industrial impact.
Ways to stand out from the crowd:
Experience with CAD kernels or formats such as Parasolid, ACIS, Open Cascade, CATIA, NX, Creo, SOLIDWORKS, STEP, IGES, B-Rep, NURBS, or spline-based representations.
Experience with tetrahedral, hexahedral, polyhedral, anisotropic, adaptive, boundary-layer, curved, or high-order mesh generation.
Work in isogeometric analysis, remeshing, meshless methods, topology optimization, shape optimization, differentiable geometry, or AI-native mesh generation.
Experience with geometry repair, feature or simulation-intent recognition, parameterization, persistent correspondence, learning-based geometry representations, GPU spatial algorithms, or solver-aware adaptation.
You will also be eligible for equity and benefits.
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.Skills Required
- PhD or equivalent experience in computer science, computational geometry, scientific computing, graphics, applied mathematics, computational mechanics, engineering, or related field
- 5+ years of experience
- Proven background in computational geometry, geometry processing, mesh generation, adaptive discretization, CAD and CAE algorithms, or simulation-ready representations (5+ years)
- C++ skills
- Python skills
- Understanding of boundary representations, topology, mesh quality, discretization error, numerical robustness, and solver requirements, evidenced by research, software, or industrial impact
- Experience with CAD kernels or formats such as Parasolid, ACIS, Open Cascade, CATIA, NX, Creo, SOLIDWORKS, STEP, IGES, B-Rep, NURBS, or spline-based representations
- Experience with tetrahedral, hexahedral, polyhedral, anisotropic, adaptive, boundary-layer, curved, or high-order mesh generation
- Experience in isogeometric analysis, remeshing, meshless methods, topology/shape optimization, differentiable geometry, or AI-native mesh generation
- Experience with geometry repair, feature or simulation-intent recognition, parameterization, persistent correspondence, or learning-based geometry representations
- Experience with GPU spatial algorithms or solver-aware adaptation
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
