Connect machine learning with numerical algorithms at NVIDIA, advancing solver performance for scientific, engineering, and industrial simulations.

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Python and GPU computing to AI-native research.

This senior applied research scientist role focuses on ML methods inside and around numerical solvers. You will develop AI-guided multigrid, solver-in-the-loop learning, differentiable simulation, and hybrid numerical or ML methods. The work spans NVIDIA platforms, NVIDIA Research, universities, and industrial simulation teams.

Key Responsibilities

Research AI-assisted numerical methods for large-scale simulations

Invent learned preconditioners and AI-guided multigrid methods

Build solver-in-the-loop pipelines using simulation data

Measure convergence,



robustness, correctness, and end-to-end speedup

Collaborate across NVIDIA numerical and simulation platforms

Requirements

PhD or equivalent experience in a related field

Five years of relevant work or research experience

Machine learning and scientific computing background

Experience with PyTorch, JAX, Python, and GPU computing

Knowledge of PDEs, sparse linear algebra, and iterative solvers

Apply scientific machine learning, numerical algorithms, and GPU computing to NVIDIA’s AI-native solver research.

📌 Senior AI Numerical Methods Scientist (Winnipeg)
🏢 NVIDIA
📍 Winnipeg