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Physical Intelligence

Develops AI-driven robotic systems for industry

Mechatronics Intern

Hardware Systems IntegrationConfirmed live in the last 24 hours
Internship · Winter 2027San Francisco, CA, USANo salary listedIn-person

About the Job

Requirements

  • Completed 3A Mechanical or Mechatronics Engineering; students below 3A with exceptional projects, GitHub portfolios, and work experience will also be considered.
  • Strong mechanical and electromechanical design fundamentals, including dynamic loading, finite element analysis, and design for manufacturing.
  • Familiarity with embedded and mechatronic systems, including Controller Area Network, brushless direct-current control, and motor controllers.
  • Proficiency in computer-aided design using NX, Onshape, or SolidWorks, and rapid-prototyping tools including stereolithography and fused deposition modeling 3D printing and laser cutting.
  • Basic electrical design and debugging, including schematics, soldering, and test equipment.
  • Data-driven debugging and test planning using Python, MATLAB, or similar tools.

Responsibilities

  • Design, build, and test subsystems for robotic arms and mobile platforms, including actuation, sensing, enclosures, and power systems.
  • Work hands-on with prototypes in the hardware lab, including bring-up, assembly, calibration, and reliability testing of arms, lifts, mobile bases, and end-effectors.
  • Integrate inputs from mechanical, electrical, and software teams to root-cause mission-critical issues and improve system performance.
  • Analyze deployment data to inform design iterations and reliability improvements.
  • Own the full stack of a subsystem from computer-aided design through printed circuit board and bench testing.

Desired Qualifications

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

About the Company

Physical Intelligence

Develops AI-driven robotic systems for industry

Physical Intelligence develops AI-powered robotic systems by creating general-purpose AI models and learning algorithms to control physically actuated devices in the real world, serving industries like manufacturing, healthcare, and logistics. Its approach uses foundational models and learning-based control software that runs on robots to sense, decide, and act in real environments, often integrated with hardware and partner networks. The company differentiates itself by focusing on foundational AI methods for physical robotics and partnering with high-profile investors to support both R&D and commercialization across multiple industries. Its goal is to bring AI-driven robotic systems into real-world operations to boost efficiency and productivity through scalable, learnable robotics.

Company size
201-500
Company stage
Late Stage VC
Total funding
$2.1B
Headquarters
San Francisco, CA
Founded
2024

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