Senior Applied Research Scientist – AI Native Numerical Methods

This is a fully remote position, open to applicants in California, +3 more states.

📋 Description

• Conduct research on AI-assisted numerical techniques to enhance convergence, stability, accuracy, robustness, and wall-clock performance for large-scale simulations.

• Develop learned coarse spaces, learned preconditioners, AI-guided multigrid methods, sequence-aware solver strategies, solver-control policies, differentiable solver components, and hybrid numerical/ML algorithms.

• Establish solver-in-the-loop pipelines utilizing residual histories, discretized operators, meshes, geometry, simulation outputs, performance counters, and physics constraints.

• Create evaluation metrics that assess convergence rate, failure rate, conservation, memory footprint, correctness, and overall speedup.

• Engage in collaboration across various domains including numerical methods, CUDA-X, Warp, PhysicsNeMo, NVIDIA Research, CAE, EDA, semiconductor, electronics, thermal-fluid, electromagnetics, and digital twin workflows.

• Contribute to shaping NVIDIA's applied research strategy 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.

• A minimum of 5 years of relevant work or research experience.

• Strong background in machine learning and scientific computing, demonstrating the integration of ML methodologies with numerical algorithms.

• Proficiency in PyTorch, JAX, or similar deep learning frameworks, along with expertise in Python and GPU computing.

• Familiarity with PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element/finite volume methods, optimization, or differentiable programming.

• A research background in scientific machine learning, numerical linear algebra, or differentiable simulation integrated with numerical solvers.

• Effective communication skills that facilitate collaboration among AI research, numerical methods, product, and production software teams.

• Proven track record showing that ML techniques have enhanced real numerical solvers by achieving faster convergence, fewer failures, increased robustness, or reduced costs in industrial-scale simulations.

• Experience with AI-guided multigrid, reduced-order components within solver algorithms, neural operators linked to solver workflows, or automated solver management.

• Familiarity 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.

🏝️ Benefits

• Equity

• Benefits

• Inclusive work environment

• Equal opportunity employer