---
title: 'Mechatronics Intern at Physical Intelligence'
canonical: 'https://feeny.ai/job/mechatronics-intern-physical-intelligence-san-francisco-nz7d9xzh1v4y'
type: 'job'
last_seen: '2026-10-18'
---

# Mechatronics Intern at Physical Intelligence

- **Company:** Physical Intelligence
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-17
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/physicalintelligence/0bcf909e-b38b-4276-91a1-e55c4c56a33a

## Job description

Fundamentally, a builder. Someone who loves to make robots real. We want someone who’s as comfortable debugging a CAN bus issue as they are machining a bracket — an individual who thinks in systems, and who gets excited by tight integration between mechanical, electrical, and embedded design. You’ll join the core Hardware Engineering team at Physical Intelligence who is supporting the robots that power PI’s data collection and model training operations. The Team The core Hardware Engineering team designs and builds the robotic arms, mobile platforms, and end-effectors that underpin PI’s data collection and model training operations. They work across mechanical, electrical, embedded, and software disciplines to iterate quickly on prototypes, bring up new subsystems, and keep the fleet performant in real-world environments. In This Role You Will

- Subsystem Design & Test: Design, build, and test key subsystems for various robotic arms and mobile platforms — including actuation, sensing, enclosures, and power systems.
- Hands-On Prototyping: Work hands-on with prototypes in our hardware lab, including bring-up, assembly, calibration, and reliability testing of arms, lifts, mobile bases, and end-effectors.
- Cross-Disciplinary Integration: Integrate cross-disciplinary inputs from mechanical, electrical, and software teams to root-cause mission-critical issues and drive high-level system performance.
- Data-Driven Iteration: Analyze data from deployments to inform design iteration and reliability improvements.
- Full-Stack Subsystem Ownership: Own the full stack of a subsystem from CAD to PCB to bench testing.

## What We Hope You’ll Bring

- Completed 3A Mechanical or Mechatronics Engineering. Students below 3A with exceptional projects, GitHubs, portfolios, and work experience will also be considered.
- Strong mechanical and electromechanical design fundamentals (dynamic loading, FEA, DFM).
- Familiarity with embedded and mechatronic systems (CAN, BLDC control, motor controllers).
- Proficiency in CAD (NX, OnShape, SolidWorks) and rapid prototyping tools (3D printing — SLA and FDM — laser cutting).
- Basic electrical design and debug (schematics, soldering, test equipment).
- Data-driven debugging and test planning (Python, MATLAB, or similar).

Bonus Points

- Exposure to manufacturing or assembly environments.
- Prior experience in robotics, autonomous systems, or hardware startups.

The duration of this internship is January-May 2027.

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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