---
title: 'NPI Technical Program Manager at Physical Intelligence'
canonical: 'https://feeny.ai/job/npi-technical-program-manager-physical-intelligence-san-francisco-8grzx6hq5pm4'
type: 'job'
last_seen: '2026-09-06'
---

# NPI Technical Program Manager at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-07
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/404111a6-3a18-4a7c-a249-dd787f975188

## 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.

We are seeking a driven and resourceful Technical Program Manager to lead the charge on bringing our robot platforms online. This is your opportunity to sit at the heart of the action and make the next generation of hardware systems ready for researchers and deployments.

You'll own the end-to-end program execution, including but not limited to development schedules, risk mitigation, build matrix management, material forecasting and planning, Clear-to-Build (CTB) analysis, logistics readiness and troubleshooting, vendor follow-up, and configuration management across our hardware programs. You'll partner closely with hardware engineering, supply chain, build, fab, and fleet integration to communicate changes, analyze risk, and execute complex build plans in support of fast-moving research and deployment timelines.

In this role you will

- Partner with Platform leads and the engineering team to drive program schedule, timelines and execution end to end.
- Own build planning and production coordination for hardware platforms from part / tooling kickoff through production output to fleet hand-off.
- Drive material forecasting, Clear-to-Build (CTB) analysis, dashboards, and readiness reporting to keep build schedules honest and risks visible.
- Manage hardware staging and acceptance hand-offs to the fleet integration team.
- Coordinate arrival of COTS hardware from global vendors, including modifications for repair, allocation, tracking, replenishment, and end-of-life management.
- Run logistics readiness and troubleshooting for incoming components, and chase down the missing 2% before it becomes a blocker.
- Route custom hardware requests across internal build and fabrication space and external vendors.
- Partner with supply chain and procurement on vendor follow-up, lead-time management, and risk mitigation on long-lead and single-source parts.
- Identify and implement process improvements in how PI plans, builds, and ships hardware as the program scales.

## What we hope you'll bring

- Bachelor's degree in Engineering or a related field. Experience with shipping hardware is required.
- 3+ years of experience in operations, supply chain, manufacturing program support, or engineering program management, including cross-functional and vendor engagement.
- Strong organizational and project management skills, with a proven ability to drive process improvements and manage multiple priorities in a fast-paced environment.
- Proficiency with operational tools such as Notion, Linear, Excel, project trackers, and procurement, ticketing, or workflow systems.
- Excellent written and verbal communication. Able to translate engineering detail into clear status updates for both technical and executive audiences.
- Willing to travel domestically and occasionally internationally to vendors and contract manufacturers.
- Experience in R&D operations, robotics, or other technical environments with complex procurement, NPI, or capitalization requirements.
- Familiarity with electromechanical systems, PCBA, machined parts, or robotics hardware.
- Direct experience with build planning, BOM management, Clear-to-Build analysis, or material readiness for hardware programs.
- Hands-on bias: a willingness to spend time on the build floor working alongside technicians and engineers.
- Ability to identify and implement process improvements using data and cross-functional input.
- Familiarity with ERP/MRP, PLM, or production tracking tools.

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
- [People Ops](https://feeny.ai/job/people-ops-physical-intelligence-san-francisco-k10k2h0mgp8j) — San Francisco, CA
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- [Supply Chain Lead](https://feeny.ai/job/supply-chain-lead-physical-intelligence-san-francisco-9n0bq8qkcvzm) — San Francisco, CA