AI-Driven Solver Scientist for Numerical Methods

NVIDIA Gruppe; California (MO); 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 connect machine learning with numerical algorithms, aiming to speed up solvers for large-scale simulations. The role covers ML-guided multigrid, differentiable simulation, and solver-in-the-loop learning across NVIDIA's solver, simulation, and computational engineering platforms.

Requirements include a PhD or equivalent, 5 years of experience, and proficiency with PyTorch/JAX, Python, and GPU computing. Equity and benefits are provided.

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 deep learning 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 machine learning, numerical linear algebra, or differentiable simulation integrated with numerical solvers.
• Communication skills that support collaboration across AI research, numerical methods, product, and production software teams.

Responsibilities

• Research AI-assisted numerical methods that improve convergence, stability, accuracy, robustness, and wall-clock performance for large-scale scientific, engineering, and industrial simulations.
• Invent learned coarse spaces, learned preconditioners, AI-guided multigrid methods, sequence-aware solver strategies, solver-control policies, differentiable solver components, and hybrid numerical / 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 that measure 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.

Skills

PhD or equivalent

5 years experience

ML & scientific computing

PyTorch/JAX

Python & GPU computing

PDEs

Sparse linear algebra

Iterative solvers

Preconditioning

Finite element / finite volume

Differentiable programming

Communication skills

Education

PhD or equivalent

Tools

PyTorch

JAX

Python

GPU computing

Job description

NVIDIA is seeking an applied researcher to connect machine learning with numerical algorithms, aiming to speed up solvers for large-scale simulations. The role covers ML-guided multigrid, differentiable simulation, and solver-in-the-loop learning across NVIDIA's solver, simulation, and computational engineering platforms.

Requirements include a PhD or equivalent, 5 years of experience, and proficiency with PyTorch/JAX, Python, and GPU computing. Equity and benefits are provided.