ML Infra Engineer, Data Systems

Job summary

Responsibilities

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…

• 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.

What they're looking for

• 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.

Tech stack

• Ray
• Spark