AI-Native Numerical Methods Scientist

NVIDIA; Pennsylvania; On-site; USD 192,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an applied researcher to bridge machine learning with numerical algorithms for faster, more reliable solvers on modern GPUs.

The role focuses on AI-guided multigrid, solver-in-the-loop learning, differentiable simulation, and hybrid numerical/ML methods, with collaboration across NVIDIA Research, CUDA-X, Warp, and partner teams.

You will help shape NVIDIA's applied research agenda for AI-native numerical methods and solver intelligence, with equity and benefits included.

Qualifications

• PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or related field.
• 5 years of relevant work/research experience.
• Background in machine learning and scientific computing with evidence of connecting ML methods to numerical algorithms.
• Experience with PyTorch, JAX, or comparable frameworks, plus Python and GPU computing.
• Working knowledge of PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element/finite volume methods, optimization, or differentiable programming.
• Research record in scientific ML, numerical linear algebra, or differentiable simulation integrated with numerical solvers.
• Strong communication skills for collaboration across AI research, numerical methods, product, and production software teams.

Responsibilities

• Research AI-assisted numerical methods to improve convergence, stability, accuracy, robustness, and wall-clock performance for large-scale simulations.
• Invent learned coarse spaces, learned preconditioners, AI-guided multigrid, solver-control policies, and differentiable solver components.
• Build solver-in-the-loop pipelines using residual histories, operators, meshes, geometry, outputs, and physics constraints.
• Define evaluation methods measuring convergence rate, failure rate, conservation, memory footprint, and end-to-end speedup.
• Collaborate with teams across numerical methods, CUDA-X, Warp, PhysicsNeMo, NVIDIA Research, CAE, EDA, semiconductor, and digital twin workflows.
• Help define NVIDIA's applied research agenda for AI-native numerical methods and solver intelligence.

Skills

PhD or equivalent

Python

PyTorch/JAX

GPU computing

Numerical methods

Communication

Education

PhD in CS/ML/Scientific computing

Tools

PyTorch

JAX

CUDA-X

Job description

NVIDIA is seeking an applied researcher to bridge machine learning with numerical algorithms for faster, more reliable solvers on modern GPUs.

The role focuses on AI-guided multigrid, solver-in-the-loop learning, differentiable simulation, and hybrid numerical/ML methods, with collaboration across NVIDIA Research, CUDA-X, Warp, and partner teams.

You will help shape NVIDIA's applied research agenda for AI-native numerical methods and solver intelligence, with equity and benefits included.