---
title: 'Fullstack Software Engineer at Physical Intelligence'
canonical: 'https://feeny.ai/job/fullstack-software-engineer-physical-intelligence-san-francisco-4d81qeq8e7hy'
type: 'job'
last_seen: '2026-09-06'
---

# Fullstack Software Engineer at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-21
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/031e9b1e-6e58-4c81-b608-3cfda0514082

## 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 Fullstack Software Engineer, you will build internal products that help Pi's research and operations teams move faster. You will work closely with researchers, prototypers, operators, and other engineers to turn operational workflows into reliable software.

The Team

Fullstack builds the internal software the rest of Pi runs on. Our users span the path from research intent to physical execution: researchers designing experiments and requesting data, Research Ops working across feasibility, task design, environments, prototyping, and instructions, and Production Ops running data collection, evals, and deployments across our lab, warehouse, and real-world deployments.

Annotation sits at the front of that chain, determining what our models learn from the data we collect, and this is where this role will start.

In This Role You Will

- Own annotation tooling: Build the platform and workflows for annotation generation, from the interfaces annotators work in to the pipelines behind them, and translate requirements from researchers into an actionable plan and the software to execute it.
- Make annotation legible: Build the systems that track quality, cost, throughput, and coverage so researchers can see what they are getting and decide what to change.
- Own researcher request and planning workflows: Redesign how researchers and prototypers turn ambiguous research needs into executable work, and create the software layer for understanding capacity and making tradeoffs across competing priorities.
- Build research and ops-facing tooling: Dataset browsing, eval dashboards, and the throughput, quality, and progress tracking Production Ops needs across our lab, warehouse, and real-world sites.
- Act as your own PM: Gather requirements, prioritize work, define success metrics, write specs, ship tools, drive adoption, and iterate based on feedback.
- Ship production-quality software: Build reliable frontend interfaces, backend APIs, data models, dashboards, and cloud services. You should be comfortable shipping and supporting production-grade services.

## What We Hope You'll Bring

- Strong full stack engineering experience building production web applications and APIs, especially with React, TypeScript, and Python.
- Experience with relational databases (we use Postgres), analytical systems (ClickHouse), and queueing systems.
- Familiarity with cloud and containerized environments such as GCP and Kubernetes.
- Comfort designing workflows where people, models, and software have to function as one system.
- Strong product judgment and attention to detail.
- Experience working directly with users, iterating from feedback, and navigating ambiguous workflows and evolving requirements.
- Ability to start with a practical v0 and build toward scalable, production-quality systems.

Bonus Points

- Former founder, early employee, or other demonstration of comfort with ambiguity and solving hard problems end to end.
- Experience building data labeling platforms, human-in-the-loop tools, or other data-centric AI systems.
- Experience building internal tools specifically for research, robotics, or operationally-intensive problems.
- Experience with tasking systems, instruction management, scheduling, resource allocation, or workflow orchestration.
- 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

## Other roles at Physical Intelligence

- [ML Infra Engineer, Data Systems](https://feeny.ai/job/ml-infra-engineer-data-systems-physical-intelligence-san-francisco-8g386zwjvd64) — San Francisco, CA
- [ML Infra Engineer, Modeling](https://feeny.ai/job/ml-infra-engineer-modeling-physical-intelligence-san-francisco-dt7e182daxxa) — San Francisco, CA
- [Software Engineer, Data Quality](https://feeny.ai/job/software-engineer-data-quality-physical-intelligence-san-francisco-p7f1n4g3j35e) — San Francisco, CA
- [Shift Lead](https://feeny.ai/job/shift-lead-physical-intelligence-san-francisco-23rb2r5nr82f) — San Francisco, CA
- [People Ops](https://feeny.ai/job/people-ops-physical-intelligence-san-francisco-k10k2h0mgp8j) — San Francisco, CA
- [NPI Technical Program Manager](https://feeny.ai/job/npi-technical-program-manager-physical-intelligence-san-francisco-8grzx6hq5pm4) — San Francisco, CA
- [Robot Operator](https://feeny.ai/job/robot-operator-physical-intelligence-san-francisco-gq9jsnxd3s5g) — San Francisco, CA
- [Manufacturing Engineer](https://feeny.ai/job/manufacturing-engineer-physical-intelligence-fremont-30v59z9v8ze2) — Fremont, CA
- [Production Test Engineer](https://feeny.ai/job/production-test-engineer-physical-intelligence-san-francisco-8ngeahchx0hx) — San Francisco, CA
- [Supply Chain Lead](https://feeny.ai/job/supply-chain-lead-physical-intelligence-san-francisco-9n0bq8qkcvzm) — San Francisco, CA