---
title: 'Applied Researcher at Physical Intelligence'
canonical: 'https://feeny.ai/job/applied-researcher-physical-intelligence-san-francisco-9djzhmxnt63v'
type: 'job'
last_seen: '2026-11-08'
---

# Applied Researcher at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-01-23
- **Last confirmed live:** 2026-11-08
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/1a7a181f-c318-4e0b-9516-c7111b3e3968/application

**Skills:** Python, Robotics, Autonomous Systems, Perception, Policy, Control, Hardware, Data Curation, Neural Network Debugging, Robot Manipulation Platforms

> Deploy and debug learned policies on physical robots while training and tuning models to improve real-world performance. Bridge research and operations by translating advances into deployable systems and collaborating with partners and operators.

## 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. The Team The Deployments team is responsible for solving real world problems with our models and robots. We tackle the full problem space: integrating with customer workflows, training models to solve their dexterous tasks, and ensuring the on-site reliability of the system. This breadth of problem space is why we’re a full-stack robotics team - whether it’s thinking about customer facing experiences or fine-tuning models for tasks no robot has done before, we put forth the best solution Pi has to offer. In This Role You Will

- Deploy and debug learned policies on physical robots, diagnosing failures across the full stack (perception, policy, control, hardware).
- Train and tune policies, curate data, and iterate to improve real-world performance.
- Write production-quality code that interfaces with Pi’s infrastructure.
- Work with operators to set up tests, evals, and data collection pipelines.
- Engage with partners to understand use cases and observe robots in deployment contexts.
- Bridge research and operations: translate research advances into deployable systems, and surface real-world failure modes back to researchers and (software and hardware!) engineers.
- Define and shape a vision for what real-world deployments will look like in the long-term

## What We Hope You’ll Bring

- Hands-on experience deploying robots or autonomous systems in real-world environments
- Strong engineering skills: clean Python, ability to interface with infrastructure, debugging instincts
- Ability to debug the full stack from perception to control
- Practical mindset: motivated by making things work, not by open-ended research
- Clear communication with researchers, operators, and occasionally partners Bonus Points If You Have
- Founded or worked at an early-stage robotics or AV company
- PhD in relevant field
- Intuition for policy training, neural network debugging, and data curation
- Experience with robot manipulation platforms

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

- [Deployment & Engagement Operations Lead](https://feeny.ai/job/deployment-engagement-operations-lead-physical-intelligence-san-francisco-rm51qjgc5bnt) — San Francisco, CA
- [Lead Security Engineer](https://feeny.ai/job/lead-security-engineer-physical-intelligence-san-francisco-c8snwvgksgkh) — San Francisco, CA
- [Environment Coordinator](https://feeny.ai/job/environment-coordinator-physical-intelligence-san-francisco-4gkxdz41bq8g) — San Francisco, CA
- [Environment Specialist](https://feeny.ai/job/environment-specialist-physical-intelligence-san-francisco-pc9emcb2qsb0) — San Francisco, CA
- [Environment Manager](https://feeny.ai/job/environment-manager-physical-intelligence-san-francisco-qdqkwxnvkkeg) — San Francisco, CA
- [Learning & Development Lead, Operations](https://feeny.ai/job/learning-development-lead-operations-physical-intelligence-san-francisco-rjw9n41fwmh4) — San Francisco, CA
- [General Manager, Research Operations](https://feeny.ai/job/general-manager-research-operations-physical-intelligence-san-francisco-t2n6gysx5vvr) — San Francisco, CA
- [Recruiter, Hardware](https://feeny.ai/job/recruiter-hardware-physical-intelligence-san-francisco-4n292t91sd6v) — San Francisco, CA
- [Set Team Specialist](https://feeny.ai/job/set-team-specialist-physical-intelligence-san-francisco-a1zm1e3dtf2d) — San Francisco, CA
- [ML Engineer, Platform](https://feeny.ai/job/ml-engineer-platform-physical-intelligence-san-francisco-9rgmerynps3m) — San Francisco, CA