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Physical Intelligence is building general-purpose AI for the physical world, developing foundation models and learning algorithms to power robots and physically-actuated devices. The company has made significant progress on models that can control many different robots across diverse tasks and is now building the platform to make those models accessible to the broader world.
As an ML Engineer on the API Platform team, you will own the end-to-end product experience that allows external companies to use Pi's models similarly to how developers use LLM APIs today: bring their own data, fine-tune models, evaluate them, and run low-latency inference in their own environments. You'll transform capabilities that currently require close collaboration with the Pi team into a scalable platform supporting thousands—and eventually millions—of robots.
You'll work at the intersection of AI, infrastructure, and external partners, collaborating closely with researchers and engineers to turn rapidly evolving model capabilities into reliable APIs and tools that external users (from hobbyists to large organizations) can build on. Today, onboarding new partners involves significant hands-on work; your goal is to make that increasingly unnecessary through intuitive, reliable, scalable product design.
Key responsibilities include:
- Building Pi's model API end to end: data ingestion, fine-tuning, evaluation, low-latency remote inference, partner-facing tools, and deployment integrations
- Designing systems that scale from a small number of deeply integrated partners to thousands of organizations
- Architecting reliable, multi-tenant infrastructure around rate limiting, isolation, backpressure, versioning, observability, and SLOs
- Turning partner data into a first-class product experience from upload through validation, processing, fine-tuning, and evaluation
- Building and operating low-latency inference systems for robots in real-world environments
- Working closely with researchers to turn new model capabilities into stable, usable product surfaces
- Learning from partner usage patterns and turning friction into better APIs, tooling, documentation, and abstractions
- Writing high-quality production code integrated with Pi's existing infrastructure
- Defining what the developer platform for general-purpose robotics should look like (no established playbook exists yet)
This is a software and systems role first; robotics experience is not required. The company is particularly interested in people who have built developer platforms, model APIs, or infrastructure products outside of robotics.
Requirements:
- 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 those for model fine-tuning, inference, or compute-intensive workloads
- Understanding of problems that emerge as systems scale: reliability, latency, multi-tenancy, versioning, observability, and operational complexity
- Familiarity with machine learning systems to deploy, serve, and debug models in production (ML researcher background not required)
- Strong Python skills and ability to work across infrastructure and product boundaries
- High degree of ownership; comfort taking ambiguous problems, building first versions end to end, and designing systems that don't depend on you
Bonus qualifications:
- Experience building low-latency or real-time systems (streaming, inference transport, WebSockets, QUIC, or similar)
- Experience building model-serving, inference, fine-tuning, or developer-platform infrastructure
- Experience at early-stage infrastructure, AI, robotics, or autonomous systems companies
- Familiarity with the stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React, TypeScript
- Experience with security, authentication, authorization, or multi-tenant infrastructure
