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

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