Robotic Arm Video Annotation Specialist

You will review video sessions of robotic arms completing real-world tasks and turn visual activity into structured information used to train and evaluate AI systems. The work focuses on consistent interpretation of events, system states, and outcomes.

You will use dedicated annotation tools, flag unclear moments, explain your reasoning in short notes, and complete assigned batches accurately by the required deadlines. You may also review annotations and provide quality-control feedback when requested.

• Identify actions, object interactions, state changes, task outcomes, errors, and other relevant events.
• Interpret action sequences, system states, cause-and-effect relationships, and success or failure conditions.
• Tag video content accurately and consistently using detailed annotation and grading guidelines.
• Review existing annotations and provide constructive quality-control feedback when requested.

This is an entry-level, part-time contractor role for people with a professional or academic background in game design, gameplay development, technical game design, game quality assurance, or a related field. Experience with data annotation, visual quality assurance, AI training projects, or robotics is helpful but not required.

• Pay: $50-$90 USD per hour.
• Time: 20 or more hours per week.
• Work arrangement:
  Remote contractor work.
• Location:
  
  Available across North America, Latin America, Europe, the United Kingdom, and Oceania.
• Language:
  English.
• Required background:
  Game design, gameplay development, technical game design, game QA, or a related field.
• Required abilities:
  Visual reasoning, close attention to detail, consistent use of detailed criteria, and clear written communication.
• Relevant experience:
  Reviewing, testing, debugging, or documenting gameplay behavior.
• Helpful but not required:
  Data annotation, visual quality assurance, AI training, or robotics experience.

AI training work is the human side of building artificial intelligence. People review videos and other data, label what they see, and explain patterns so AI systems can learn to recognize actions, states, and outcomes.