# ML Infra Engineer, Data Systems at Physical Intelligence

ML Infra Engineer, Data Systems at Physical Intelligence in San Francisco. On-site, full-time. Posted September 1, 2026.

- Company: Physical Intelligence
- Location: San Francisco
- Workplace: On-site
- Job type: Full-time
- Posted: 2026-09-01
- Apply: https://jobs.ashbyhq.com/physicalintelligence/1f6cf3eb-f70f-480b-9fff-befd56c7fd07/application
- Source: https://jobmagic.ai/jobs/ml-infra-engineer-data-systems-physical-intelligence-k53wfc

## Requirements

- 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.
- Hands-on experience operating petabyte-scale datasets.
- Debugging and fixing performance bottlenecks in data-heavy systems.

## Description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
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.
**The Team**
The Infrastructure organization builds the foundations that make large-scale learning possible at PI. This includes training systems, data platforms, evaluation pipelines, and the tooling that allows researchers and roboticists to work with massive datasets safely and efficiently.
**In This Role You Will**

- **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.

**What We Hope You’ll Bring**

- 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.

**Bonus Points If You Have**

- 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.

- Hands-on experience operating petabyte-scale datasets.

- Debugging and fixing performance bottlenecks in data-heavy systems.

*Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.*