---
title: 'Software Engineer, Robot Interfaces at Physical Intelligence'
canonical: 'https://feeny.ai/job/software-engineer-robot-interfaces-physical-intelligence-san-francisco-r7xp0by9w54z'
type: 'job'
last_seen: '2026-10-18'
---

# Software Engineer, Robot Interfaces at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-24
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/86237337-963a-4e0e-bcf7-82d366e5f0fc

## 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 operators, roboticists, researchers, and other engineers to turn physical 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 task design, environments, prototyping, and instructions, and Production Ops running collection, evals, and deployments across our lab, warehouse, and real-world sites.

In This Role You Will

- Own the human-robot interfaces: The software people use to run, monitor, and debug robots, from setup and calibration through live operation and review. Someone lives in this for an entire shift, and small friction compounds fast.
- Design for the whole shift, not just the episode: Time on a robot is a fraction of the day. The rest is preparation, troubleshooting, handoffs, and feedback, spread today across several tools and several people. The goal is not to expose every tool, but to build one surface that tells someone what deserves their attention now and notices when they're stuck.
- Design for safety: Make state unambiguous, make failure obvious, and make the safe action the easy one. Assume the network drops mid-episode or a camera dies, and design so the system degrades predictably rather than surprisingly.
- Own the realtime edge: Video, robot state, and control at latency budgets tight enough that teleoperation feels direct, plus the controllers, headsets, pedals, and sensors people drive with.
- Draw the line between device and cloud: Decide what has to stay local because it is safety-critical or latency-sensitive, and what belongs in a control plane that owns identity, state, and coordination across sites.
- Ship into the physical world: On-prem and on-device deployment, versioning and rollout across lab, warehouse, and partner sites, and enough observability to know what is running where.
- Make the work measurable: Instrument these interfaces well enough that we can see where the operation loses time, then test changes against it.
- Act as your own PM: Gather requirements, prioritize, define success metrics, ship, and drive adoption. That starts by standing in the room and watching a shift.

## What We Hope You'll Bring

- Strong fullstack engineering experience building production web applications and APIs, especially with React, TypeScript, and Python.
- Realtime web experience: WebRTC, WebSockets, streaming video, state synchronization, and the debugging that comes with all of it.
- Comfort with binary wire formats and schema-defined interfaces: protobuf, gRPC, or similar. Most of what these interfaces talk to is not a REST API.
- Experience shipping software that runs on machines you do not administer: on-prem or on-device deployment, degraded-network operation, versioning across a fleet.
- Strong UX instincts for high-stakes, high-frequency interfaces, where the user is standing up, under time pressure, and a mistake has physical consequences.
- Comfort designing workflows where people, hardware, and software have to function as one system.
- Strong product judgment, and experience working directly with users through ambiguous workflows and evolving requirements.

Bonus Points

- Former founder, early employee, or other demonstration of comfort with ambiguity and solving hard problems end to end.
- Experience with teleoperation, robotics, or other robot-adjacent software.
- Experience with safety-critical or industrial systems (control room, medical device, aviation, automotive, or similar) where interface decisions carry real consequences.
- Hardware-adjacent background: firmware, embedded, device integration, or having shipped software alongside a physical product.
- Experience with workflow, dispatch, or scheduling systems that direct human work in the physical world.
- Experience with our specific stack: React, TypeScript, Python, Postgres, 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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