Machine Learning Infrastructure Engineer

Join an AI for science team building large-scale models for physical systems. You will develop the infrastructure behind distributed training and inference, helping the team advance models that can predict outcomes and inform effective actions.

• Design, deploy, and maintain distributed machine learning training and inference clusters.
• Build scalable end-to-end pipelines for petabyte-scale datasets and model training across the machine learning lifecycle.
• Evaluate parallelization methods and numerical precision trade-offs at different model scales.
• Profile and debug low-level GPU operations to improve performance.
• Track relevant research and apply promising ideas to infrastructure and model workflows.

• 2 to 10 years of experience building large-scale machine learning infrastructure for pretrained or foundation models, including compute, data, inference, and distributed training systems.
• Experience optimizing large-scale training, data, or inference workloads, including low-level GPU performance work with CUDA and JAX.
• Proficiency with distributed training frameworks such as FSDP or Deep Speed.
• Experience with cloud platforms such as GCP, AWS, or Azure, and with containers and orchestration tools such as Docker and Kubernetes.
• Familiarity with scalable model serving, distributed task management, monitoring, logging, observability, and version control practices.
• Background in physical AI or AI for science, such as robotics, autonomous systems, or biology, and an interest in working across the machine learning lifecycle.
• Comfort working in a fast-paced environment and learning unfamiliar domains.

Base salary range: $200,000 to $400,000 annually. Visa sponsorship is not available.

LOCATION Remote within the United States, with San Francisco, California as the primary location.