Senior Applied Research Scientist - AI Native Numerical Methods

NVIDIA; Canada; On-site; CAD 269,000 - 499,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation seeks an applied researcher to bridge machine learning with numerical algorithms, accelerating solvers for large-scale simulations. The role spans ML inside numerical solvers, AI-guided multigrid, differentiable simulation, and solver-in-loop learning, in collaboration with NVIDIA Research, CUDA-X, and engineering teams.

Ideal candidates hold a PhD or equivalent with 5+ years of experience, proficiency in PyTorch/JAX, Python, and GPU computing, and a strong background in PDEs,

Qualifications

• PhD or equivalent in computer science, machine learning, scientific computing, or related field.
• 5 years of relevant work/research experience.
• Strong background connecting ML methods to numerical algorithms.
• Experience with PyTorch, JAX, or comparable frameworks; Python & GPU computing.
• Knowledge of PDEs, sparse linear algebra, iterative solvers, or differentiable programming.

Responsibilities

• Research AI-assisted numerical methods to improve convergence, stability, and speed of large-scale simulations.
• Invent learned coarse spaces, preconditioners, and AI-guided solver strategies.
• Build solver-in-the-loop pipelines using residual histories and discretized operators.
• Define evaluation methods for convergence, robustness, and end-to-end speedup.
• Collaborate with CUDA-X, Warp, NVIDIA Research, and related teams.
• Help define NVIDIA's applied research agenda for AI-native numerical methods.

Skills

ML & scientific computing

Python

GPU computing

Communication

Collaboration

Education

PhD or equivalent

Tools

PyTorch

JAX

Job description

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.

We are working across NVIDIA solver, simulation, and computational engineering platforms, with collaborators NVIDIA Research, universities, and industrial simulation teams. 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.

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.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:

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 .

Applications for this job will be accepted at least until November 7, 2026.

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.