Machine Learning Infrastructure Engineer

Join an AI team building large-scale models for physical systems, with weather as an initial application. You will develop the distributed infrastructure needed to train and serve foundation models, helping advance AI that can predict outcomes and identify effective actions.

• Design, deploy, and maintain distributed machine learning training and inference clusters.

• Build scalable end-to-end pipelines for petabyte-scale datasets and manage workloads across the machine learning lifecycle.

• Research and evaluate training approaches, including parallelization methods and numerical precision trade-offs at different model scales.

• Profile and debug low-level GPU operations to improve performance.

• Follow relevant research and apply promising ideas to infrastructure and model workflows.

On-site in San Francisco, California. Relocation support is provided.

• Two to ten years of experience building large-scale machine learning infrastructure for pretrained or foundation models.

• Hands-on experience with compute clusters, data infrastructure, distributed training, and inference systems.

• Expertise optimizing large-scale training, data, or inference workloads, including low-level GPU performance analysis.

• Experience with distributed training frameworks such as FSDP or DeepSpeed; familiarity with CUDA, JAX, Python, C++, Linux, or NVIDIA hardware is valuable.

• Knowledge of cloud platforms such as GCP, AWS, or Azure, and containerization and orchestration tools such as Docker and Kubernetes.

• Background in physical AI or AI for science, such as robotics, autonomous systems, or biology, and comfort working across the machine learning lifecycle.

• Familiarity with scalable task management and model serving, observability practices, and experiment analysis such as ablation studies.

• A track record of working effectively in fast-paced environments and on time-sensitive projects.

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