Job description

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. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 192,000 USD - 304,750 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 21, 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

Build algorithms for computational geometry, computer-aided engineering and design interoperability, mesh generation, mesh adaptation, spatial data structures, and curved discretization.
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.
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.
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.
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 .
Applications for this job will be accepted at least until August 21, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipython

Platform: eightfold
Effort: medium