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Physical Intelligence Logo Physical Intelligence · IT - Software Development

ML Infra Engineer (Data Systems)

United States, San Francisco · Job Posted February 21, 2026
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Job Description

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.

Job Responsibility

  • Data Ingestion & Processing: Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data
  • Batch & Streaming Systems: Operate large-scale batch and streaming workflows over massive datasets
  • 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
  • Data Movement: Move hundreds of terabytes to petabytes efficiently across clusters and environments
  • Operational Correctness: Implement observability, validation, and guardrails to prevent silent data regressions
  • Cross-Functional Collaboration: 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

Nice to have

  • 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
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