---
title: 'ML Engineer - API Platform at Physical Intelligence'
canonical: 'https://feeny.ai/job/ml-engineer-api-platform-physical-intelligence-san-francisco-ze5fj5gk238m'
type: 'job'
last_seen: '2026-10-11'
---

# ML Engineer - API Platform at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-05
- **Last confirmed live:** 2026-10-11
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/6fe1ce2d-303b-4d34-9c13-4cb6476f4cbe

## Job description

## Who We Are

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future. We’ve made significant progress toward building models that can control many different robots across a wide range of tasks. Now, we’re building the platform that will make those models accessible to the broader world. As an API Product Engineer, you’ll build the product surface that allows other companies to use Pi’s models much like developers use an LLM API today: bring their own data, fine-tune models, evaluate them, and run low-latency inference in their own environments. You’ll own this experience end to end, turning capabilities that today require close collaboration with our team into a platform that can eventually support thousands—and ultimately millions—of robots.

The Team Software Engineering at Pi builds the systems that scale research breakthroughs into reliable products in the physical world. 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.

In This Role You Will This is a software and systems role first. Robotics experience is not required, and we’re particularly excited about people who have built developer platforms, model APIs, or infrastructure products outside of robotics.

- 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.

## What We Hope You'll Bring

- 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. We’re especially interested in people who have shipped platforms for model fine-tuning, inference, or other compute-intensive workloads.
- An understanding of the problems that emerge as systems scale: reliability, latency, multi-tenancy, versioning, observability, and operational complexity.
- Enough familiarity with machine learning systems to deploy, serve, and debug models in production. You do not need to be an ML researcher.
- 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.

Bonus Points If You Have

- Experience building low-latency or real-time systems, including streaming, inference transport, WebSockets, QUIC, or similar technologies.
- Experience building model-serving, inference, fine-tuning, or developer-platform infrastructure.
- Experience at an early-stage infrastructure, AI, robotics, or autonomous systems company.
- Familiarity with our stack: Python, Postgres, ClickHouse, GCP, Kubernetes, Modal, React, and TypeScript.
- Experience with security, authentication, authorization, or multi-tenant infrastructure.

What This Role Is Not

- This is not a Customer Success role. You won’t own ongoing partner support or change management inside customer organizations.
- This is not a Partner Roboticist or Applied Research role. You won’t be responsible for training policies for individual partners or debugging robots on site.
- This is not a traditional ML infrastructure role focused primarily on training systems, compilers, or accelerator scheduling. You’ll work closely with those systems, but your focus is the product and platform through which the outside world uses Pi’s models.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

## About Physical Intelligence

## Company Overview
- **One-liner**: Physical Intelligence is building general-purpose AI foundation models that can control any robot to perform any physical task, aiming to bring the flexibility of large language models into the physical world.
- **Entity Type**: Private (Series A/B stage; total funding $1.07B across multiple rounds)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Sergey Levine (UC Berkeley), Chelsea Finn (Stanford), Karol Hausman (CEO, ex-Google DeepMind), Lachy Groom (ex-Stripe), Quan Vuong (ex-Google DeepMind), Brian Ichter, and Adnan Esmail

## Core Business
- **Primary industry**: Robotics foundation models / Artificial Intelligence / Research Services
- **Target customers**: B2B enterprise – logistics, grocery, manufacturing, and other verticals requiring physical automation (currently testing with a small number of partners)
- **Mission**: “Bringing general-purpose AI into the physical world” – developing learning algorithms and foundation models to power robots and other physically-actuated devices

## Products & Services
- **π0 (pi-zero)**: First generalist policy released in October 2024; a vision-language-action (VLA) model capable of dexterous multi-task robot control. Open-sourced in February 2025.
- **π0.5**: VLA with open-world generalization (April 2025); can control a mobile manipulator to clean unfamiliar kitchens or bedrooms.
- **π0.7**: Steerable robotic foundation model with emergent capabilities (April 2026); can control a new robot platform without additional training.
- **π*0.6**: VLA trained with reinforcement learning to improve success rate and throughput on real-world tasks (November 2025).
- **FAST**: Efficient robot action tokenizer enabling 5x faster training of generalist policies (January 2025).
- **Multi-Scale Embodied Memory (MEM)**: Gives models long- and short-term memory for tasks exceeding ten minutes (March 2026).
- All offerings are research-stage models; the company does not currently sell a commercial product but partners with select companies for testing.

## Market Standing
- **Valuation**: $5.6 billion (as of January 2026, per TechCrunch)
- **Total Funding**: $1.07 billion across four rounds:
  - Seed (March 2024): $70M led by Lux Capital and Thrive Capital
  - Series A (November 2024): $400M led by Lux Capital and Thrive Capital
  - Venture Round (November 2025): $600M from 8 investors
  - Venture Round (March 2026): undisclosed amount from 4 investors
- **Notable Investors/Partners**: Khosla Ventures, Lux Capital, Thrive Capital, Sequoia Capital, OpenAI, CapitalG, Redpoint Ventures, Bond
- **Growth Signals**: Headcount grew 183.5% year-over-year to 163 employees (LinkedIn, mid-2026); the company states it has “blown through” its original 5-10 year roadmap in just 18 months; test deployments in logistics, grocery, and food production.

## Competitive Advantages
- **Cross-embodiment learning**: Models can transfer knowledge to any new robot hardware without starting data collection from scratch, lowering the marginal cost of onboarding autonomy.
- **Pure research focus**: Unlike competitors (e.g., Skild AI), Physical Intelligence deliberately avoids near-term commercialization, allowing the team to pursue general intelligence without product pressure.
- **World-class founding team**: Combination of top robotics academics (Levine, Finn) and experienced entrepreneurs/operators (Groom, Hausman).
- **Open-source release**: π0 weights and code are publicly available, fostering community contributions and accelerating research.

## Strategic Focus
- Current priorities include scaling foundation models to more tasks and environments, improving generalization and robustness, and expanding partnerships for real-world testing.
- The company explicitly does not give investors a timeline for monetization; instead it focuses on building general-purpose physical intelligence with a 5-10 year horizon (though progress has been faster than expected).
- Future strategy centers on continuous improvement via a loop of data collection → training → evaluation → more data, rather than rapid deployment.

## Why Work Here
- **Culture**: Described as a “pure company” – internally driven by research needs, not external market demands. Highly collaborative, with a mix of engineers, scientists, and roboticists working together.
- **Work Environment**: Hybrid/in-office – most roles require on-site presence in San Francisco (396 Treat Ave), though some remote flexibility exists. In-office setting with a “no reception” vibe, open lab space with robot stations.
- **Growth**: Team has scaled from ~80 (Jan 2026) to 163 (mid-2026) and is still hiring across research, ML infra, hardware, and engineering roles. Plans to grow “as slowly as possible” to maintain quality.
- **Perks & Engineering Highlights**: Access to cutting-edge robotics hardware and compute resources; opportunity to publish research and open-source code; exposure to a wide variety of real-world automation challenges (e.g., robots learning to fold pants, peel vegetables, make espresso). Employees come from top institutions like Berkeley, Stanford, Google, NVIDIA, and Anduril.

## Sources
1. [physicalintelligence.company](https://www.physicalintelligence.company/) – Official website, model releases, and values
2. [TechCrunch](https://techcrunch.com/2026/01/30/physical-intelligence-stripe-veteran-lachy-grooms-latest-bet-is-building-silicon-valleys-buzziest-robot-brains/) – In-depth profile including valuation, strategy, and culture
3. [LinkedIn](https://www.linkedin.com/company/physical-intelligence) – Company details, headcount, funding, and talent sources
4. [Built In](https://builtin.com/company/physical-intelligence) – Career page, employee count, and office policy
5. [jobs.ashbyhq.com/physicalintelligence](https://jobs.ashbyhq.com/physicalintelligence) – Current job openings

## Other roles at Physical Intelligence

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