Senior Applied Research Scientist – GPU Native Numerical Algorithms

🕒 August 13

🏄 California, New York, +2 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🍂 Massachusetts – Remote 🔔 Pennsylvania – Remote

💵 $192k - $356.5k / year

⏰ Full Time

🟠 Senior

🧬 Research Scientist

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NVIDIA

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10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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 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 • 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 research prototypes into NVIDIA software capabilities • Help 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 • 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

🏖️ Benefits

• Equity • Benefits

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