Senior Applied Research Scientist – AI Native Numerical Methods

🕒 August 13

🏄 California, New York, +2 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🍂 Massachusetts – Remote 🔔 Pennsylvania – Remote

💵 $192k - $356.5k / year

⏰ Full Time

🟠 Senior

🧠 AI Research Scientist

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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 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 • 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 • 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

🏖️ Benefits

• Equity • Benefits

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