Client Roles
ABOUT THE ROLE
Join an AI team developing large foundation models for physical systems, with weather as a core application. You will build the infrastructure that enables scalable model training and inference, helping advance AI for science and physical AI.
WHAT YOU'LL DO
- Design, deploy, and maintain distributed machine learning training and inference clusters.
- Build scalable end-to-end pipelines for large datasets and model training across the machine learning lifecycle.
- Test training approaches, including parallelization methods and numerical precision trade-offs across model scales.
- Profile and debug low-level GPU operations to improve performance.
- Follow relevant research and apply useful ideas to infrastructure and training workflows.
WHAT WE'RE LOOKING FOR
- Two to ten years of experience building large-scale machine learning infrastructure for foundation models, including compute clusters, data infrastructure, inference, and distributed training.
- Experience optimizing large-scale training, data, or inference workloads, with expertise in distributed training frameworks such as FSDP or DeepSpeed.
- Proficiency with Python and C++, and experience with GPU performance optimization, CUDA, or JAX.
- Familiarity with cloud platforms such as GCP, AWS, or Azure, plus Docker, Kubernetes, and scalable model serving or task management systems.
- Background in physical AI or AI for science, such as robotics, biology, or other science-focused applications.
- Experience across the machine learning lifecycle, including analyzing training experi