Senior Applied Research Scientist – GPU Native Numerical Algorithms
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Job Description
Applied Research Scientist developing GPU-native numerical algorithms for NVIDIA engineering simulation. Designing solver methods and research prototypes across GPU architectures, simulation workloads, and AI-native computational engineering.
Responsibilities:
- Invent and reformulate numerical algorithms co-designed for modern NVIDIA GPU architectures, including implicit and explicit engineering simulation
- Develop linear and nonlinear solver approaches, including Newton-Krylov, multigrid and AMG, domain decomposition, matrix-free algorithms, mixed precision, sparse iterative and direct methods, and preconditioning
- Investigate reformulation or replacement of CPU-oriented numerical methods for GPU architectures
- Evaluate algorithms on mechanics, contact, thermal-fluid systems, electromagnetics, semiconductor process and device simulation, EDA, multiphysics, and related CAE workloads
- Collaborate with CUDA-X, Warp, solver engineering, NVIDIA Research, universities, and industry partners to move research prototypes into NVIDIA software capabilities
- Shape the applied research roadmap for GPU-native numerical methods and their evolution toward AI-native computational engineering
Requirements:
- PhD or equivalent experience in computational mechanics, applied mathematics, scientific computing, computer science, aerospace, mechanical, civil engineering, or a related technical field
- 5+ years of relevant work/research experience
- Research or engineering experience with PDE discretization, finite element, finite volume, discontinuous Galerkin methods, nonlinear solvers, sparse linear algebra, preconditioning, or high-performance computing
- Experience writing numerical software in C++ and Python, plus experience developing or optimizing CUDA or GPU code
- Experience using profiling, benchmarking, numerical validation, or performance analysis to improve algorithms on GPU or multi-GPU systems
- Ability to communicate technical tradeoffs clearly and collaborate across research, engineering, product, and partner teams
- Experience with implicit structural dynamics, nonlinear mechanics, contact, CFD, electromagnetics, multiphysics, semiconductor simulation, EDA, CAE, or CAD-connected engineering workflows
- Contributions to or practical experience with PETSc, Trilinos, MFEM, libCEED, OpenFOAM, NVIDIA Warp, CUDA-X, cuSPARSE, cuSOLVER, or related computational science frameworks
- Experience with industrial simulation, EDA, semiconductor, CAE, or CAD ecosystems, including Ansys, Abaqus, LS-DYNA, Siemens Simcenter, Dassault SIMULIA, Altair, Cadence, Synopsys, COMSOL, MathWorks, or comparable internal solver and design platforms
- Experience with distributed solvers using MPI, NCCL, asynchronous methods, or performance analysis on GPU clusters
- Publications, patents, open-source work, or deployed software in computational science venues or communities such as SC, SIAM CSE, SIAM SISC, CMAME, IJNME, JCP, AIAA, USNCCM, WCCM, or related areas
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