Senior Applied Research Scientist – AI Native Numerical Methods
Core
Inventing AI-native numerical algorithms that combine machine learning with classical scientific and engineering solvers on modern GPU architectures to improve convergence, stability, accuracy, and performance for large-scale simulations.
Role type
Senior Applied Research Scientist (AI-native numerical methods)
Builds
AI-guided multigrid methods, learned preconditioners, solver-in-the-loop pipelines, and hybrid numerical/ML algorithms for scientific and industrial simulation.
Domain
Scientific computing, numerical linear algebra, and industrial simulation on GPU architectures.
Deliverable
production ML models
Required skills
Machine learning, scientific computing, PyTorch, JAX, Python, GPU computing, PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element/volume methods, optimization, differentiable programming
Preferred skills
AI-guided multigrid, reduced-order modeling, neural operators, differentiable simulation, PDE-constrained learning, inverse design, uncertainty quantification, Bayesian methods, reinforcement learning for solver control
Technologies
PyTorch, JAX, CUDA, Python, GPU architectures
Responsibilities
Research AI-assisted numerical methods to improve solver performance; Invent learned coarse spaces, solver-control policies, and hybrid algorithms; Build solver-in-the-loop pipelines using residual histories and physics constraints; Define evaluation methods for numerical impact; Collaborate with numerical methods, CUDA-X, and physics teams; Define NVIDIA's applied research agenda for solver intelligence.
Seniority
Senior, hands-on IC
