Machine Learning Engineer (API Platform)

Physical Intelligence; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Physical Intelligence in San Francisco seeks an engineer to build the model API end-to-end, from data ingestion to low-latency inference, for external partners.

You'll work with researchers to turn new model capabilities into stable surfaces, design scalable, secure multi-tenant systems, and empower developers with tools and documentation.

This role emphasizes ownership, production-grade code, and delivering reliable, scalable robotics software.

Qualifications

• Experience deploying ML systems to production environments.
• Ability to design scalable developer platforms.
• Familiarity with security and multi-tenant infrastructure.
• Experience with model fine-tuning or inference workloads.
• Ability to write high-quality production code.

Responsibilities

• Build Pi’s model API end to end with data ingestion and fine-tuning.
• Design scalable multi-tenant infrastructure with observability and SLOs.
• Collaborate with researchers and engineers to turn model capabilities into APIs.
• Develop partner-facing tooling and deployment integrations.
• Write production-grade code with deep integration to existing infra.

Skills

Python

Distributed systems

Production systems

Model serving

DevOps / infrastructure

Ownership

APIs

Security / multi-tenant

Full-stack (React/TS)

Tools

Kubernetes

PostgreSQL

ClickHouse

GCP

TypeScript

React

Job description

• 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 researcherAn understanding of the problems that emerge as systems scale: reliability, latency, multi-tenancy, versioning, observability, and operational complexityStrong software engineering fundamentals and experience building production systemsStrong Python skills and the ability to work comfortably across infrastructure and product boundariesA 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 youExperience building and scaling developer platforms. We’re especially interested in people who have shipped platforms for model fine-tuning, inference, or other compute-intensive workloadsDeep backend and systems experience across APIs, services, databases, caching, distributed systems, and infrastructureExperience with security, authentication, authorization, or multi-tenant infrastructureFamiliarity with our stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React, and TypeScriptExperience building model-serving, inference, fine-tuning, or developer-platform infrastructureExperience at an early-stage infrastructure, AI, robotics, or autonomous systems companyExperience building low-latency or real-time systems, including streaming, inference transport, WebSockets, QUIC, or similar technologies