ML Infra Engineer, Data Systems
San Francisco • FullTime
Posted 13d ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
Physical Intelligence is seeking an ML Infra Engineer specializing in Data Systems to build and operate the data infrastructure powering large-scale robot learning. This role is crucial for bridging raw data sources and training/evaluation processes, aiming to accelerate development while ensuring performance, correctness, and reliability at scale. It's a systems-focused position at the intersection of distributed systems, storage, and machine learning infrastructure, contributing to the foundational platforms that enable large-scale learning.
Responsibilities
- Design and build high-throughput pipelines for validating, transforming, and featurizing raw multimodal data.
- Operate large-scale batch and streaming workflows over massive datasets.
- Design object storage layouts, metadata systems, and efficient access patterns, selecting appropriate file formats.
- Build systems for data lifecycle management, including backfills, dataset rebuilds, garbage collection, and large-scale transformations.
- Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce training time.
- Build scalable metadata stores for datasets, annotations, and training artifacts.
- Efficiently move petabytes of data across clusters and environments.
- Implement observability, validation, and guardrails to prevent silent data regressions.
- Collaborate with cross-functional teams of researchers, engineers, and roboticists to translate data needs into robust systems.
Requirements
- 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.
- Ownership mindset: design, build, operate, and iterate on systems end-to-end.
- Enjoy working closely with researchers and unblocking fast-moving projects.