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 a strong ownership mindset, comfort with performance and storage efficiency, and experience with distributed systems or large-scale data pipelines.

Missions

• 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.

Profil recherché

- Ownership mindset: design, build, operate, and iterate on systems end-to-end

- Comfort reasoning about performance, memory, I/O, and storage efficiency

- Enjoy working closely with researchers and unblocking fast-moving projects

- Experience building distributed systems or large-scale data pipelines

- Experience with object storage systems and data format tradeoffs

- Strong software engineering fundamentals

- Experience with large ML training pipelines or dataloading systems

- Knowledge of columnar or custom data formats

- Experience with systems like ClickHouse, Ray, Flink, Spark, or similar

- Debugging and fixing performance bottlenecks in data-heavy systems

- Hands-on experience operating petabyte-scale datasets