ML Infra Engineer (Data Systems)

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.

- 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.