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About the Role

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.

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.

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.

Skills

Large ML training pipelines
Dataloading systems
Columnar data formats
Custom data formats
ClickHouse
Ray
Flink
Spark
Petabyte-scale datasets
Debugging performance bottlenecks
Fixing performance bottlenecks in data-heavy systems

About the Company

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.
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.

Equal Opportunity

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