AI Robotics Data Trainer

You will operate a handheld gripper device in a laboratory and complete repeated manipulation tasks across different objects and environments. Tasks may include picking up objects, opening containers, and completing simple household actions.

You will follow written protocols closely so each task produces consistent robot-learning data. The work also includes resetting physical environments, checking submissions, reporting problems, and coordinating with project leads and other contributors.

• Operate a handheld gripper to complete manipulation tasks.
• Follow detailed step-by-step protocols without deviation.
• Reset physical environments between attempts.
• Check submissions for quality and identify damaged recordings or hardware problems.
• Record observations, report issues promptly, and suggest process improvements.
• Maintain accuracy and focus during repetitive hands-on work.

What It Pays and Takes

This is part-time, on-site contractor work in San Francisco. Previous robotics or AI experience is not required.

• Pay: $30 per hour.
• Location: On-site in San Francisco; open to candidates in the United States.
• Language: English fluency required.
• Work type: Contractor and part-time.
• Strong fine motor skills, dexterity, and precise handling of physical objects.
• Careful attention to detail and the ability to follow complex instructions exactly.
• Reliability, punctuality, and personal accountability in professional, academic, or gig-based work.
• Physical stamina, comfort with repetitive tasks, and the ability to learn basic hardware interfaces.
• Clear written and verbal communication.

Helpful Background

Experience in warehouse work, operations, gaming, gig work, or student projects can be useful preparation. These backgrounds may help with hands-on tasks, repeated procedures, or careful work with physical equipment.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training work is the human side of building artificial intelligence. People collect, prepare, and review examples that help AI systems learn, and experienced contributors are valued for accurate work and subject knowledge.