---
title: 'Software Engineer, AI Productivity at Physical Intelligence'
canonical: 'https://feeny.ai/job/software-engineer-ai-productivity-physical-intelligence-san-francisco-40amqvbc87dp'
type: 'job'
last_seen: '2026-09-13'
---

# Software Engineer, AI Productivity at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-11
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/7f8397a4-c3cd-45c6-90c7-13c4481f4699

## Job description

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. As a Software Engineer focused on AI productivity, you will build and roll out the tools that help us use AI effectively across the company. You will work closely with engineering, research, operations, people, and other teams to understand how people work, identify where AI can create leverage, and turn those opportunities into reliable internal tools and workflows. The Team Runtime owns core systems that help π’s operations and research teams move quickly and reliably. This role will sit in Runtime and work alongside engineers focused on build systems, infrastructure, and developer productivity. Your focus will be AI tooling: making AI agents, assistants, integrations, and automation useful across the company. You will partner deeply with teams across PI to understand their workflows, build tools that fit how they work, and drive adoption until those tools become part of the operating rhythm of the company. In This Role You Will

- Own AI tooling adoption across π: Identify where AI tools can improve velocity, build or integrate the right solutions, teach teams how to use them, and drive adoption.
- Build internal AI tooling and integrations: Build backend services, scripts, workflows, user interfaces, LLM integrations, and agent infrastructure.
- Make AI agents ergonomic: Own workflows for cloud agents, agent management, and internal automation that are easy to use, easy to monitor, and easy to trust.
- Build tools for engineering, research, and operational velocity: Help engineers use AI to write, test, debug, review, and validate code faster. Empower researchers to extract signals and iterate quickly and confidently. Work with operations and recruiting to understand their workflows and build tools that give them leverage.
- Own best practices and enablement: Create playbooks, examples, onboarding, office hours, demos, and shared workflows that help people learn from the best AI users at π.
- Partner on security and data access: Ensure AI tools have the right access to be useful while respecting data boundaries, permissions, and company policies.
- Evaluate build vs. buy: Maintain a strong perspective on the AI tooling ecosystem, evaluate commercial tools, and recommend what π should adopt.
- Measure impact: Define success metrics for adoption, productivity, and satisfaction. Use feedback and data to understand what is working, what is not, and where to invest.

## What We Hope You'll Bring

- Strong software engineering fundamentals and the ability to ship quickly.
- Deep excitement about AI tools and strong opinions about how they should be used.
- Hands-on fluency with AI coding workflows and modern LLM-based tools.
- Technical flexibility: ability to build backend services, internal tools, integrations, automation, and user interfaces.
- Strong product judgment and taste for developer experience and internal tooling.
- High empathy and excitement to work across engineering, research, operations, recruiting, and other teams.
- Ability to learn unfamiliar systems quickly and operate across many technical domains.
- Good judgment around security, permissions, data access, and safe tool rollout.
- Clear communication, documentation, and teaching ability.
- Comfort driving adoption, not just writing code. Bonus Points
- Experience building developer tools, agents, or automation platforms.
- Experience building internal tools specifically for research, robotics, or operationally-intensive problems.
- Experience with our specific stack: React, TypeScript, Python, Postgres, ClickHouse, GCP, and Kubernetes.

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

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