ML Infra Engineer, Data Systems

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.

As an ML Infra Engineer (Data Systems), you’ll build and operate the data infrastructure that powers large-scale robot learning. Your systems will sit directly between raw data sources and training/evaluation, enabling us to move faster while maintaining performance, correctness, and reliability at scale.

This is a systems role at the intersection of distributed systems, storage, and machine learning infrastructure.

The Infrastructure organization builds the foundations that make large-scale learning possible at PI. This includes training systems, data platforms, evaluation pipelines, and the tooling that allows researchers and roboticists to work with massive datasets safely and efficiently.

• Strong software engineering fundamentals.

• Experience building distributed systems or large-scale data pipelines.

• Comfort reasoning about performance, memory, I/O, and storage efficiency.

• Familiarity with batch and/or streaming processing systems.

• Experience with object storage systems and data format tradeoffs.

• Ownership mindset: design, build, operate, and iterate on systems end-to-end.

• Enjoy working closely with researchers and unblocking fast-moving projects.

• Experience with large ML training pipelines or dataloading systems.

• Knowledge of columnar or custom data formats.

• Experience with systems like ClickHouse, Ray, Flink, Spark, or similar.

• Hands-on experience operating petabyte-scale datasets.

• Debugging and fixing performance bottlenecks in data-heavy systems.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.