ML Engineer - API Platform
San FranciscoFull-timePosted 2w ago
About the role
Physical Intelligence is building a platform to expose models as developer-friendly APIs for robotics and physically-actuated devices. This role focuses on creating end-to-end model APIs, scaling the developer platform, and delivering low-latency, multi-tenant infrastructure for external partners to fine-tune, evaluate, and run inferences with Pi’s models.
Responsibilities
- Build Pi’s model API end to end, including data ingestion, fine-tuning, evaluation, low-latency remote inference, partner-facing tools, and deployment integrations.
- Design systems that can scale from a small number of deeply integrated partners to thousands of organizations and potentially millions of robots.
- Architect reliable, multi-tenant infrastructure around rate limiting, isolation, backpressure, versioning, observability, and SLOs.
- Turn partner data into a first-class product experience, from initial upload through validation, processing, fine-tuning, and evaluation.
- Build and operate low-latency inference systems that allow Pi models to control robots running in real-world environments.
- Work closely with researchers to turn new model capabilities into stable, usable product surfaces.
- Learn from how partners use the platform and turn recurring friction into better APIs, tooling, documentation, and abstractions.
- Write high-quality production code that integrates deeply with Pi’s existing infrastructure.
- Define what the developer platform for general-purpose robotics should look like. There is no established playbook for this yet.
Qualifications
- Strong software engineering fundamentals and experience building production systems.
- Deep backend and systems experience across APIs, services, databases, caching, distributed systems, and infrastructure.
- Experience building and scaling developer platforms, especially for model fine-tuning, inference, or compute-intensive workloads.
- Understanding of reliability, latency, multi-tenancy, versioning, observability, and operational complexity as systems scale.
- Familiarity with machine learning systems to deploy, serve, and debug models in production (not required to be an ML researcher).
- Strong Python skills and ability to work across infrastructure and product boundaries.
- High degree of ownership and ability to prototype end-to-end solutions and then design scalable systems.
Skills
Required:Software engineering fundamentalsBackend and systemsDeveloper platform experiencePythonOwnership / autonomous problem solving
Nice to have:ML deployment basics
Last updated Oct 14, 2026. Interested? Apply on the company site.
About Physical Intelligence
Physical Intelligence (π)
Physical Intelligence is bringing general-purpose AI into the physical world.
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