Physical Intelligence
ML Infra Engineer, Data Systems
San Francisco
Sponsorship not specifiedDetected 46 days ago
Distributed SystemsAlgorithmsMachine LearningSparkCollaboration
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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Physical Intelligence is bringing general-purpose AI into the physical world.
- 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
- Data Ingestion & Processing: Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data.
- Storage Systems: Design object storage layouts, metadata systems, and efficient access patterns; choose file formats with performance and scalability in mind.
- Data Lifecycle Management: Build systems for backfills, dataset rebuilds, garbage collection, and large-scale transformations.
- Training-Time Performance: Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival → model training.
- Metadata & Indexing: Build scalable metadata stores for datasets, annotations, and training artifacts.
- Operational Correctness: Implement observability, validation, and guardrails to prevent silent data regressions.
- Experience building distributed systems or large-scale data pipelines.
- Ownership mindset: design, build, operate, and iterate on systems end-to-end.
Nice to have
- Strong software engineering fundamentals.
- 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.
- 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.
Benefits
- Bonus Points If You Have
Company info
- 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.
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