The role
From Nvidia's own posting.
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.