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 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.

What We Hope You’ll Bring

Strong software engineering fundamentals.

Experience building distributed systems or large-scale data pipelines.

ML Infra Engineer (Data Systems)

Responsibilities

Qualification

Required

Preferred

Company

Physicalintelligence

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