Senior Applied Research Scientist – GPU Native Numerical Algorithms

NVIDIA

Applied Research Scientist designing GPU-native numerical solvers for NVIDIA’s accelerated-computing platforms. Advancing simulation algorithms toward production software and AI-native engineering methods.

Posted 8/13/2026full-timeRemote • California, Massachusetts, New York, Pennsylvania • 🇺🇸 United StatesSenior💰 $192,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and optimizing numerical algorithms for modern GPU architectures, with a strong focus on high-performance computing and applied research in computational mechanics. Proficient in collaborating across multidisciplinary teams to advance GPU-native numerical methods and their applications in engineering simulations.

Highest-signal resume keywords

PhD In Computational MechanicsC++ And Python ProgrammingCUDA Or GPU Code OptimizationExperience With Numerical Software DevelopmentResearch Experience In PDE Discretization

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Numerical Algorithms DevelopmentLinear And Nonlinear Solver ApproachesSparse Linear AlgebraHigh-Performance ComputingPDE DiscretizationFinite Element MethodsMixed Precision TechniquesPerformance AnalysisMatrix-Free MethodsPreconditioning Strategies

Soft Skills

Technical CommunicationCollaboration Across Teams

Tools & Technologies

CUDA-XNVIDIA WarpPETScTrilinosOpenFOAMCuSPARSECuSOLVERMPINCCLAnsys

Industry Keywords

Computational ScienceEngineering SimulationMultiphysicsElectromagneticsSemiconductor SimulationCAECAD EcosystemsCFDContact MechanicsIndustrial Simulation

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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, multigrid and AMG, domain decomposition, matrix-free, mixed precision, sparse iterative and direct methods, and preconditioning strategies
Investigate GPU-native alternatives to CPU-oriented numerical methods, including synchronization-avoiding Krylov, GPU-native multigrid and domain decomposition, matrix-free implicit, mixed-precision, and sparse direct/iterative hybrid methods
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

What you’ll need

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 is an advantage
Contributions to or practical experience with PETSc, Trilinos, MFEM, libCEED, OpenFOAM, NVIDIA Warp, CUDA-X, cuSPARSE, cuSOLVER, or related computational science frameworks is advantageous
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 platforms is advantageous
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

Benefits

Comp & perks

Equity
Benefits 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score