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Physical Intelligence
San Francisco, California, United States
Source: Physical Intelligence careers · View original posting
From Physical Intelligence's posting. “We” and “our” refer to the employer.
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.
In This Role You Will
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.
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