Senior Applied Research Scientist – GPU Native Numerical Algorithms

Overview

• NVIDIA pioneered accelerated computing.
• Today, we are building software, systems, and research platforms that help scientists and engineers solve problems that were once out of reach.
• We are looking for an Applied Research Scientist to join our computational engineering applied research team! In this role, we will work together to design GPU-native numerical methods that make engineering simulation faster, more reliable, and easier to use across NVIDIA platforms, while providing the numerical foundations for emerging AI-native engineering algorithms.
• You will explore solver algorithms, build research prototypes, compare approaches on representative workloads, and help move promising ideas into software used by researchers, engineers, and partners.
• The goal is not simply to port established CPU algorithms, but to rethink methods around massive parallelism, hierarchical memory, reduced synchronization, mixed precision, tensor-core computation, and multi-GPU systems.
• This role connects numerical analysis, accelerated computing, production-minded software engineering, and the co-design of future AI-native engineering methods.
• We are interested in candidates who enjoy working across math, code, hardware, and real engineering applications.
• Come help us shape the future of simulation on GPUs! What you'll be doing: We work as a team, and you will help us: 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.
• What we need to see: 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.
• Ways to stand out from the crowd: 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.
• Your base salary will be determined based on your location, experience, and other objective job-related factors.
• The expected base salary ranges for this position are: 121,275 EUR – 210,210 EUR for Level 4; and 147,675 EUR – 255,970 EUR for Level 5.