GPU-Native Numerical Methods Architect for AI Simulation

NVIDIA; New York (NY); On-site; USD 192,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an Applied Research Scientist for the computational engineering applied research team. You will design GPU-native numerical methods to accelerate engineering simulation across NVIDIA platforms, building prototypes and evaluating solver algorithms for multi-GPU systems.

The role blends numerical analysis, high-performance software engineering, and co-design of AI-native methods. You will collaborate with CUDA-X, Warp, NVIDIA Research, universities, and industry partners to move

Qualifications

• 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.

Responsibilities

• Invent and reformulate numerical algorithms whose mathematical and computational structure is co-designed for modern NVIDIA GPU architectures, including 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 established CPU-oriented numerical methods should be reformulated or replaced for GPU architectures, including new approaches to 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 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.

Skills

C++

Python

CUDA

GPU programming

Profiling & performance analysis

Education

PhD or equivalent in computational mechanics / related field

Tools

PETSc

Trilinos

MFEM

libCEED

OpenFOAM

NVIDIA Warp

CUDA-X

cuSPARSE

cuSOLVER

Job description

NVIDIA is seeking an Applied Research Scientist for the computational engineering applied research team. You will design GPU-native numerical methods to accelerate engineering simulation across NVIDIA platforms, building prototypes and evaluating solver algorithms for multi-GPU systems.

The role blends numerical analysis, high-performance software engineering, and co-design of AI-native methods. You will collaborate with CUDA-X, Warp, NVIDIA Research, universities, and industry partners to move