Senior Applied Research Scientist – GPU Native Numerical Algorithms

We are looking for a Senior Applied Research Scientist to join our computational engineering applied research team. You will design GPU-native numerical methods that make engineering simulation faster, more reliable, and easier to use across NVIDIA platforms.

Responsibilities:

• Invent and reformulate numerical algorithms for modern NVIDIA GPU architectures
• 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
• 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

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