Senior Applied Research Scientist – GPU Native Numerical Algorithms

This is a fully remote position, open to applicants in California, +3 more states.

📋 Description

• Create and enhance numerical algorithms specifically designed for contemporary NVIDIA GPU architectures, encompassing both implicit and explicit engineering simulations.

• Design linear and nonlinear solver techniques, including but not limited to Newton-Krylov, multigrid and AMG, domain decomposition, matrix-free approaches, mixed precision, sparse iterative and direct methods, as well as preconditioning strategies.

• Explore GPU-native alternatives to traditional CPU-focused numerical methods, such as synchronization-avoiding Krylov, GPU-native multigrid and domain decomposition, matrix-free implicit methods, mixed precision, and hybrid sparse direct/iterative techniques.

• Assess algorithms across various workloads in mechanics, contact mechanics, thermal-fluid systems, electromagnetics, semiconductor processing and device simulations, EDA, multiphysics, and related CAE areas.

• Collaborate with teams from CUDA-X, Warp, solver engineering, NVIDIA Research, academic institutions, and industry partners to transition research prototypes into NVIDIA software functionalities.

• Contribute to the development of the applied research roadmap for GPU-native numerical methods and their progression towards 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 discipline.

• Over 5 years of pertinent work or research experience.

• Research or engineering experience involving PDE discretization, finite element methods, finite volume methods, discontinuous Galerkin methods, nonlinear solvers, sparse linear algebra, preconditioning, or high-performance computing.

• Proficiency in writing numerical software using C++ and Python.

• Experience in developing or optimizing code for CUDA or GPU systems.

• Familiarity with profiling, benchmarking, numerical validation, or performance analysis aimed at enhancing algorithms on GPU or multi-GPU architectures.

• Strong ability to clearly communicate technical trade-offs and work collaboratively across research, engineering, product, and partner teams.

• Experience in implicit structural dynamics, nonlinear mechanics, contact mechanics, CFD, electromagnetics, multiphysics, semiconductor simulations, EDA, CAE, or CAD-connected engineering workflows is a plus.

• Contributions to or practical experience with frameworks such as PETSc, Trilinos, MFEM, libCEED, OpenFOAM, NVIDIA Warp, CUDA-X, cuSPARSE, cuSOLVER, or other related computational science tools is beneficial.

• Familiarity with industrial simulation, EDA, semiconductor, CAE, or CAD ecosystems, including platforms like Ansys, Abaqus, LS-DYNA, Siemens Simcenter, Dassault SIMULIA, Altair, Cadence, Synopsys, COMSOL, MathWorks, or similar is advantageous.

• Experience with distributed solvers utilizing MPI, NCCL, asynchronous methods, or performance analysis on GPU clusters.

• Publications, patents, contributions to open-source projects, or deployed software in computational science fields or communities.

🏝️ Benefits

• Equity

• Benefits