---
title: 'People Ops at Physical Intelligence'
canonical: 'https://feeny.ai/job/people-ops-physical-intelligence-san-francisco-k10k2h0mgp8j'
type: 'job'
last_seen: '2026-09-06'
---

# People Ops at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-14
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/58bd9bea-c447-4c85-8514-0e4486331683

## 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.

The Team

You will sit within the People team, reporting to the HRBP and working closely with recruitment, the Head of People, and our Business Operations lead. The People team is small and high-ownership: we build the systems and programs that support PI's workforce as the company scales from startup to industry leader. Your operational precision will directly enable the team to move faster and stay compliant.

In This Role You Will

- Onboarding: Design and create workflows that make for simple, and accurate onboarding that support the new hire experience, customized to their role and journey. Ensure training materials are automated and accurate by population.
- Payroll Administration: Own the end-to-end payroll process for hourly and exempt employees in Rippling, including timesheet audits, time off, premium pay calculations (shift differentials, OT, double time), off-cycle corrections, and final pay compliance. Own structure around approval paths and processes, and identify/act on concerning trends or outliers.
- Benefits Administration: Manage open enrollment, qualifying life event processing, carrier data audits, EOI notices, and employee-facing benefits communications. Serve as the primary point of contact for employee benefits questions and escalations.
- HRIS and Data Integrity: Maintain Rippling as the system of record: employee records, job and compensation changes, onboarding/offboarding workflows, and reporting. Audit data regularly and resolve discrepancies with payroll and finance.
- Compliance: Monitor and execute on Federal, California, and San Francisco compliance obligations including things like new hire documents, I-9s, leave administration, fair chance notices and other reporting requirements like EEOC or California Pay Data Reporting.
- HR Operations Support: Partner with the HRBP on operational components of employee relations, leaves, accommodations, and separation workflows. Draft and maintain standard HR documents including offer letters, notices, and policy acknowledgments. Build, or re-build current processes to increase efficiency or support for employees.

## What We Hope You'll Bring

- 3-5 years of HR operations experience: in California, with exposure to both high volume hourly and exempt populations, throughout their employment experience.
- Fluency in Rippling: ability to architect and troubleshoot as needed, avoiding unexpected results
- Strong working knowledge of California wage and hour law: including overtime, meal and rest break rules, final pay requirements, and regular rate of pay calculations.
- Experience administering group health benefits: including carrier relationships, open enrollment, and QLE processing.
- Exceptional attention to detail and process ownership: you catch errors before they become problems and document your work clearly. Own the process, and rebuild it if suboptimal.
- Excited to create and refine processes to support in a high growth environment: Able to build process as we scale from 300 to 500+ employees

Bonus Points

- Familiarity with San Francisco-specific compliance requirements: including HCSO, SF PPLO, and the SF Fair Chance Ordinance.
- Prior experience at a startup or high-growth company: where you have had to build processes from scratch, or rebuilt the existing.
- Experience as an HR generalist: ability to understand how HR OPs fits into the bigger picture, and build strong, workflows.

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
- [Fullstack Software Engineer](https://feeny.ai/job/fullstack-software-engineer-physical-intelligence-san-francisco-4d81qeq8e7hy) — 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
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- [Supply Chain Lead](https://feeny.ai/job/supply-chain-lead-physical-intelligence-san-francisco-9n0bq8qkcvzm) — San Francisco, CA