Senior Applied Research Scientist – GPU Native Numerical Algorithms

NVIDIA•US, CA, Remote, MA

5d•$192,000 - $356,500

About The Position

NVIDIA is seeking an Applied Research Scientist to join its computational engineering applied research team and design GPU-native numerical methods for faster, more reliable engineering simulation across NVIDIA platforms. The role also provides numerical foundations for emerging AI-native engineering algorithms. The scientist will explore solver algorithms, build research prototypes, compare approaches on representative workloads, and help bring promising ideas into software used by researchers, engineers, and partners. The work emphasizes rethinking established methods for massive parallelism, hierarchical memory, reduced synchronization, mixed precision, tensor-core computation, and multi-GPU systems rather than simply porting CPU algorithms. The position spans numerical analysis, accelerated computing, production-minded software engineering, and co-design of future AI-native engineering methods. The successful candidate will work across mathematics, code, hardware, and real engineering applications, collaborate with internal and external teams, and contribute to the long-term applied research roadmap for GPU-native numerical methods.

Requirements

PhD or equivalent experience in computational mechanics, applied mathematics, scientific computing, computer science, aerospace, mechanical or civil engineering, or a related technical field.
At least 5 years of relevant work or 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, and 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.

Nice To Haves

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, or 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.

Responsibilities

Invent and reformulate numerical algorithms co-designed for modern NVIDIA GPU architectures, including methods for implicit and explicit engineering simulation.
Develop linear and nonlinear solver approaches, including Newton-Krylov methods, multigrid and AMG, domain decomposition, matrix-free algorithms, mixed precision methods, sparse iterative and direct methods, and preconditioning strategies.
Investigate when CPU-oriented numerical methods should be reformulated or replaced for GPU architectures, including synchronization-avoiding Krylov methods, GPU-native multigrid and domain decomposition, matrix-free implicit methods, mixed-precision algorithms, and sparse direct/iterative hybrids.
Evaluate algorithms on representative workloads in mechanics, contact, thermal-fluid systems, electromagnetics, semiconductor process and device simulation, EDA, multiphysics, and related CAE domains.
Collaborate with CUDA-X, Warp, solver engineering, NVIDIA Research, universities, and industry partners to move useful research from prototype to NVIDIA software capabilities.
Help shape the long-term applied research roadmap for GPU-native numerical methods and their evolution toward AI-native computational engineering.
Use profiling, benchmarking, numerical validation, and performance analysis to improve algorithms on GPU or multi-GPU systems.