P
Physical Intelligence
Machine Learning Engineer (API Platform)
Skills
Google Cloud PlatformReactTypescriptPythonPostgreSQLMachine LearningAIKubernetes
9 days ago
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Important information
Contract type:
Permanent
Location:
San Francisco, CA, USA
Work mode:
Onsite
Published on:
10 October 2026
What they need
- This role sits at the intersection of our AI, infrastructure, and external partners
- You’ll work closely with researchers and engineers to turn rapidly evolving model capabilities into APIs and tools that external users, from hobbyists to large organizations, can actually build on
- Today, onboarding a new partner to Pi can involve significant hands-on work
- The goal is to make that increasingly unnecessary: partners should be able to bring us data, fine-tune a model, evaluate it, and run inference through a product that is intuitive, reliable, and built to scale
- 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- Enough familiarity with machine learning systems to deploy, serve, and debug models in production. You do not need to be an ML researcher
- An understanding of the problems that emerge as systems scale: reliability, latency, multi-tenancy, versioning, observability, and operational complexity
- Strong software engineering fundamentals and experience building production systems
- Strong Python skills and the ability to work comfortably across infrastructure and product boundaries
- A high degree of ownership. You’re comfortable taking an ambiguous problem, building the first version end to end, and then designing the system so it no longer depends on you
- Experience building and scaling developer platforms. We’re especially interested in people who have shipped platforms for model fine-tuning, inference, or other compute-intensive workloads
- Deep backend and systems experience across APIs, services, databases, caching, distributed systems, and infrastructure
- Experience with security, authentication, authorization, or multi-tenant infrastructure
- Familiarity with our stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React, and TypeScript
- Experience building model-serving, inference, fine-tuning, or developer-platform infrastructure
- Experience at an early-stage infrastructure, AI, robotics, or autonomous systems company
- Experience building low-latency or real-time systems, including streaming, inference transport, WebSockets, QUIC, or similar technologies
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