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 Team

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.2

In This Role You Will

- 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.