At NVIDIA, we are using accelerated computing and machine learning to create a new generation of AI-native scientific, engineering, and industrial simulation. We are seeking an applied researcher who can connect machine learning with numerical algorithms to make solvers faster, more reliable, and more efficient. This work focuses on ML methods inside and around numerical solvers, including AI-guided multigrid, solver-in-the-loop learning, differentiable simulation integrated with AI algorithms, and hybrid numerical / ML methods. We work across NVIDIA solver, simulation, and computational engineering platforms, with collaborators NVIDIA Research, universities, and industrial simulation teams.
The primary focus of this role is to invent AI-native numerical algorithms that combine machine learning with classical scientific and engineering solvers on modern GPU architectures.
What you'll be doing:
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.
What we need to see:
PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or a 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.
Ways to stand out from the crowd:
Evidence that ML 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.
192,000 USD – 304,750 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
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 proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or a related field
- 5 years of relevant work or research experience
- Background in machine learning and scientific computing, with experience connecting ML methods to numerical algorithms
- Experience with PyTorch, JAX, or comparable deep learning frameworks
- Proficiency in 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
- Experience improving real numerical solvers through faster convergence, fewer failures, improved robustness, or lower cost
- Experience with AI-guided multigrid, reduced-order solver components, neural operators, 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, thermal-fluid simulation, electromagnetics, or digital twins
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
