ML Infra Engineer (Data Systems)

Join our team as an ML Infra Engineer (Data Systems) and play a crucial role in building and operating the data infrastructure that powers large-scale robot learning. You will work at the intersection of distributed systems, storage, and machine learning infrastructure, designing and building high-throughput pipelines, operating large-scale workflows, and optimizing training-time performance. This is a systems role that requires strong software engineering fundamentals, experience with object storage systems, and a passion for working closely with researchers and engineers.

• Design and build high-throughput pipelines for data ingestion and processing, ensuring validation, transformation, and featurization of raw multimodal data.
• Operate large-scale batch and streaming workflows over massive datasets, optimizing dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival to model training.
• Collaborate with cross-functional teams to translate evolving data needs into robust systems, ensuring operational correctness and implementing observability, validation, and guardrails to prevent silent data regressions.
• Strong software engineering fundamentals
• Experience with object storage systems and data format tradeoffs
• Comfort reasoning about performance, memory, I/O, and storage efficiency
• Familiarity with batch and/or streaming processing systems
• Experience building distributed systems or large-scale data pipelines
• Enjoy working closely with researchers and unblocking fast-moving projects
• Ownership mindset: design, build, operate, and iterate on systems end-to-end
• Knowledge of columnar or custom data formats
• Experience with large ML training pipelines or dataloading systems
• 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