We need passionate and creative people to help us build the AI frameworks underlying the NVIDIA Earth-2 platform: a comprehensive family of open models, libraries, and frameworks that democratize global access to professional-grade weather and climate AI.
As a Senior Scientific Machine Learning Engineer, you will work with some of the brightest minds in a premier AI company to develop leading machine learning frameworks, NVIDIA PhysicsNeMo and NVIDIA Earth2Studio, for our academic and industrial partners to build scientific ML technology and workflows for weather, climate, and earth system modeling.
Responsibilities:
- Work with internal project teams to validate applications built using the framework on NVIDIA’s products, and integrate new functionalities from internal or external projects into the platform
- Stay up to date with the latest research and innovations in deep learning techniques, implement and experiment with new ideas to develop and enhance NVIDIA's Earth-2 technologies, with a focus on weather & climate AI
Requirements:
- BS or MS degree (PhD preferred) in computer science, mathematics, computational science/engineering, or related technical field or equivalent experience
- 5+ yrs of relevant experience
- Strong Python programming skills
- Familiarity with containers, numeric libraries, modular software design
- Deep knowledge of state-of-the-art DNN architectures and machine learning techniques and algorithms (graph networks, diffusion models, reinforcement learning etc.) with experience in developing or using major deep learning frameworks (PyTorch, Tensorflow, JAX etc.)
- Experience with development and application of machine learning techniques to solve real world scenarios in weather/climate
- Experience with scientific visualization. Strong analytical skills with bias for action
- Good time-management and organization skills to thrive in a fast paced, dynamic environment
- Solid written and oral communications skills. Good teamwork and interpersonal skills
Ways to stand out from the crowd:
- Experience using multi-node systems with data-parallel and model-parallel programming, performance optimization. Experience with HPC programming models (OpenMPI, NCCL), and/or CUDA or GPU kernel programming
- Experience with nonlinear simulation tools and techniques, usage of major simulation codes. Published papers in the field of AI in scientific computing, especially in weather & climate applications
- Familiarity with common tooling in the Earth-2 ecosystem (xarray, zarr, regridding, weather & climate data stores, etc.)
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