Senior Applied Research Scientist – AI Native Numerical Methods

Details

At NVIDIA, this role focuses on using accelerated computing and machine learning to create AI-native scientific, engineering, and industrial simulation methods. The work connects machine learning with numerical algorithms to make solvers faster, more reliable, and more efficient, including AI-guided multigrid, solver-in-the-loop learning, differentiable simulation integrated with AI algorithms, and hybrid numerical and machine learning methods.

The primary focus is inventing AI-native numerical algorithms that combine machine learning with classical scientific and engineering solvers on modern GPU architectures. The role works across NVIDIA solver, simulation, and computational engineering platforms, with collaborators from NVIDIA Research, universities, and industrial simulation 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 and machine learning 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 convergence rate, failure rate, conservation, memory footprint, correctness, and end-to-end speedup.
• Collaborate 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.

Requirements

• PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or a related field.
• At least 5 years of relevant work or research experience.
• Background in machine learning and scientific computing, with evidence of connecting machine learning methods to numerical algorithms.
• Experience with PyTorch, JAX, or comparable deep learning frameworks, along with Python and GPU computing.
• Working knowledge of partial differential equations, 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.

Preferred Qualifications

• Evidence that machine learning methods improved real numerical solvers through faster convergence, fewer failures, improved robustness, or lower cost on industrial-scale simulations.
• Experience with AI-guided multigrid, reduced-order components inside solver algorithms, neural operators connected to solver workflows, or automated solver control.
• Experience with differentiable simulation, PDE-constrained learning, inverse design, uncertainty quantification, Bayesian methods, reinforcement learning for solver control, or automated algorithm selection.
• Publications, software contributions, or collaborations in scientific computing, matrix computations, industrial simulation, CAE, EDA, semiconductor simulation, electronic build, thermal-fluid simulation, electromagnetics, or digital twins.

Compensation and Benefits

• Base salary for Level 4: USD 192,000–304,750 per year.
• Base salary for Level 5: USD 224,000–356,500 per year.
• Eligibility for equity and benefits.
• Applications will be accepted at least until August 16, 2026.
• NVIDIA is an equal opportunity employer committed to an inclusive work environment.