Join an AI research team developing large foundation models for physical systems, starting with weather. You will build the infrastructure that enables distributed training and reliable, high-performance inference at scale.
Design, deploy, and maintain distributed machine learning training and inference clusters.
Build scalable pipelines for petabyte-scale datasets and manage models throughout the machine learning lifecycle.
Research and test training approaches, including parallelization strategies and numerical precision trade-offs.
Profile and debug low-level GPU operations to improve performance.
Follow emerging research and apply useful techniques to infrastructure and workflows.
Two to ten years of experience building large-scale infrastructure for pretrained foundation models, including compute, data, distributed training, and inference.
Experience optimizing large-scale training, data, or inference workloads, with proficiency in distributed training frameworks such as FSDP or DeepSpeed.
Hands-on experience with low-level GPU performance optimization and debugging, including CUDA and JAX.
Familiarity with cloud platforms such as GCP, AWS, or Azure, and with Docker, Kubernetes, Linux, and scalable model serving.
Background in physical AI or AI for science, such as robotics, biology, or related scientific fields, and a generalist approach across the ML lifecycle.
Strong problem-solving skills and comfort working in a fast-moving, mission-driven environment.
Salary range: $200,000 to $400,000 USD annually. Visa sponsorship is not available.
Remote within the United States. Primary location: San Francisco, California.
Job details are sourced from the employer's original posting.
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