Robotics Data Generalist

Job Title: Robotics Data Generalist

Job Type: Contract (Full-time or Part-time)

Location: United States

Data capture sessions will take place in controlled environments near major university robotics programs.

Role Overview

We are building real-world datasets used to train robotics systems.

This role involves participating in structured data capture sessions designed to generate high-quality training data for embodied AI systems. Participants will follow guided protocols to perform a variety of physical tasks in controlled environments.

This is a paid opportunity to contribute directly to the development of Physical AI.

What You’ll Do

• Participate in structured data capture sessions under guided protocols
• Perform a range of everyday interaction and manipulation tasks
• Follow detailed instructions to ensure consistency across recordings
• Help maintain organized data capture workflows
• Work alongside a small team focused on building high-quality robotics datasets

Who This Is For

We are primarily looking for students interested in robotics, AI, and embodied intelligence.

Students involved in robotics labs, manipulation research, or controls work are especially encouraged to apply.

Sessions will take place near the following universities, so anyone able to attend in-person nearby is welcome to apply:

• Stanford University

• University of California, Berkeley

• UCLA

• University of Southern California

• Carnegie Mellon University

• Georgia Institute of Technology

•The University of Texas at Austin

• University of Illinois Urbana-Champaign

• University of Michigan

• Purdue University

• Virginia Tech

• Arizona State University

What We’re Looking For

• Strong attention to detail
• Ability to follow structured protocols carefully
• Reliability and consistency across repeated tasks
• Interest in robotics and physical AI systems
• Availability for scheduled data capture sessions Previous robotics experience is helpful but not required.

Additional Details

• Paid hourly
• Small, selective cohort
• Multi-session participation preferred

Participants will contribute to datasets used to advance robotics research.