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Machine Learning Engineer (API Platform) Physical Intelligence
General-purpose AI for robots and physically-actuated devices.
Permanent
San Francisco
Remote: Not specified
Salary: Not specified
> 3 years
Skills we're looking for
React
Kubernetes
TypeScript
Clickhouse
API Platform
+1
You'll be part of
191 employees
Artificial Intelligence / Machine Learning
Our take
To empower intelligent robotics and devices, Physical Intelligence is developing general purpose AI for the physical world. The company is working to create the foundation models and learning algorithms that will support smarter robots and devices today and in the future. The company is in its early stages, but it has already raised over $1 billion since its founding in 2024. Its backers include big names such as Khosla Ventures, Lux Capital, OpenAI, Sequoia Capital and Thrive Capital, indicating the high expectations placed upon the company's foundation models.
You'll be working on
- 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
We're looking for
- 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