Senior Applied Research Scientist – AI Native Numerical Methods

RemoteSeniorPosted Aug 17, 2026

PythonPyTorchJAXCUDAMachine learningDeep learningNumPyscikit-learn+7 more

About this role

Invent AI-native numerical algorithms combining machine learning with classical scientific and engineering solvers on modern GPU architectures. Research AI-assisted methods to improve convergence, stability, and performance for large-scale simulations across NVIDIA platforms.

Must have

PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or related field

5 years of relevant work or research experience

Background in machine learning and scientific computing with evidence of connecting ML methods to numerical algorithms

Experience with PyTorch, JAX, or comparable deep learning frameworks plus Python and GPU computing

Working knowledge of PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element or finite volume methods, optimization, or differentiable programming

Research record in scientific machine learning, numerical linear algebra, or differentiable simulation integrated with numerical solvers

Communication skills supporting collaboration across AI research, numerical methods, product, and production software teams

Technologies

PythonGitLinuxMachine learningTensorFlowpandasscikit-learnPyTorchAINumPyDeep learningCI/CDLLMsJAXCUDA

Responsibilities

Research AI-assisted numerical methods that improve convergence, stability, accuracy, robustness, and wall-clock performance for large-scale simulations

Invent learned coarse spaces, learned preconditioners, AI-guided multigrid methods, sequence-aware solver strategies, solver-control policies, and hybrid numerical or ML algorithms

Build solver-in-the-loop pipelines using residual histories, discretized operators, meshes, geometry, simulation outputs, performance counters, and physics constraints

Define evaluation methods measuring numerical impact including convergence rate, failure rate, conservation, memory footprint, correctness, and end-to-end speedup

Collaborate with teams across numerical methods, CUDA-X, Warp, PhysicsNeMo, NVIDIA Research, CAE, EDA, semiconductor, electronics, thermal-fluid, electromagnetics, and digital twin workflows

Help define NVIDIA's applied research agenda for AI-native numerical methods and solver intelligence

Benefits

EquityHealth insuranceDental insuranceVision insuranceRetirement benefits with matchCompetitive salary and stock option planFlexible PTOProfessional development

Recruitment process

Work across NVIDIA solver, simulation, and computational engineering platforms with collaborators from NVIDIA Research, universities, and industrial simulation teams

Applications accepted at least until August 16, 2026

This posting is for an existing vacancy

NVIDIA uses AI tools in its recruiting processes

NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer