ML Infra Engineer (Data Systems)

Physical Intelligence • San Francisco, CA

3d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data.
• Operate large-scale batch and streaming workflows over massive datasets.
• Design object storage layouts, metadata systems, and efficient access patterns; choose file formats with performance and scalability in mind.
• Build systems for backfills, dataset rebuilds, garbage collection, and large-scale transformations.
• Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival → model training.
• Build scalable metadata stores for datasets, annotations, and training artifacts.
• Move petabytes efficiently across clusters and environments.
• Implement observability, validation, and guardrails to prevent silent data regressions.
• Work closely with cross-functional teams of researchers, engineers and roboticists to translate evolving data needs into robust systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed