---
title: 'PiBnB Logistics Coordinator at Physical Intelligence'
canonical: 'https://feeny.ai/job/pibnb-logistics-coordinator-physical-intelligence-san-francisco-xp7he0p1tm7s'
type: 'job'
last_seen: '2026-09-13'
---

# PiBnB Logistics Coordinator at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-11
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/1922d627-e2bf-4cd9-8c0f-92b919b6324f

## Job description

The PiBnB Logistics Coordinator is responsible for making the physical operations behind PiBnB run smoothly. This person coordinates the many moving pieces required to get a PiBnB location ready for data collection: bookings, props, transportation, moving services, setup logistics, and ongoing site needs. They also help improve the systems and workflows that allow PiBnB to operate across more locations with less manual coordination. This is a highly operational role. The ideal person is organized, responsive, comfortable juggling multiple priorities, and good at building relationships with a wide range of people. Core Responsibilities Bookings & Site Coordination

- Coordinate PiBnB bookings and scheduling.
- Communicate with hosts or site contacts before, during, and after bookings.
- Confirm access, timing, site requirements, and any special logistics.
- Maintain clear visibility into upcoming bookings and readiness.

Props & Site Readiness

- Coordinate which props and equipment are needed at each location.
- Ensure props are staged, transported, and available before operations begin.
- Track missing, damaged, or misplaced items.
- Coordinate replenishment or replacement as needed.

Transportation & Vans

- Coordinate vans and transportation required for each PiBnB deployment.
- Make sure vehicles are available at the right times and locations.
- Coordinate loading, unloading, and transportation of equipment and props.

Moving & Setup Services

- Coordinate movers or other external service providers when needed.
- Provide clear instructions and expectations to vendors.
- Ensure locations are set up and reset appropriately.

Operational Coordination

- Act as a central point of coordination between operators, PiBnB hosts, vendors, drivers, props teams, and internal stakeholders.
- Identify logistics problems early and resolve them quickly.
- Communicate changes clearly when plans shift.

Workflow Improvement

- Build repeatable processes for recurring PiBnB logistics.
- Identify unnecessary manual work and opportunities to simplify coordination.
- Create checklists, trackers, templates, and other lightweight systems.
- Help PiBnB scale from individually coordinated deployments toward a reliable operating system.

## What We're Looking For

The strongest candidates will demonstrate:

- Organization: Can keep many parallel details straight without dropping things.
- Anticipation: Thinks ahead about dependencies and potential failure modes.
- Ownership: Sees something that needs to happen and drives it to completion.
- Prioritization: Can distinguish an urgent blocker from something that can wait.
- Communication: Gives clear, concise updates and knows when to escalate.
- Relationship building: Works effectively with hosts, vendors, operators, and internal teams.
- Adaptability: Stays effective when plans change or unexpected problems arise.
- Process orientation: Doesn't just solve today's problem; looks for ways to make the next occurrence easier.
- Attention to detail: Checks that things are actually ready rather than assuming they are.
- Service mindset: Understands that good logistics makes everyone else's job easier.

## 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

- [Embedded Engineer](https://feeny.ai/job/embedded-engineer-physical-intelligence-san-francisco-fbf7q0tx6p00) — San Francisco, CA
- [Software Engineer, AI Productivity](https://feeny.ai/job/software-engineer-ai-productivity-physical-intelligence-san-francisco-40amqvbc87dp) — San Francisco, CA
- [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