Senior Applied Research Scientist – GPU Native Numerical Algorithms
$192,000–$304,750 year
RemoteCalifornia, United States or New York, United States
Job Summary
Design GPU-native numerical methods for engineering simulations, reformulating algorithms for massive parallelism and mixed precision while developing linear and nonlinear solver approaches like Newton-Krylov and multigrid techniques. Evaluate these approaches across mechanics, thermal-fluid systems, and semiconductor simulation workloads, then collaborate with CUDA-X, Warp, and industry partners to move prototypes into production software. Help shape the long-term applied research roadmap for AI-native computational engineering, connecting numerical analysis with accelerated computing and software engineering.
Required 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
- 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
Desired Qualifications
- 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
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