Video Data Annotator

About the role

We are seeking a full-time, on-site Video Data Annotator to support cutting-edge AI and machine learning initiatives focused on first-person and robot-mounted video data. In this role, you will analyse video footage of video clips from robot mounted cameras, accurately identifying and annotating key events based on visual observations at specific points in time. Your work will play a critical role in developing high-quality datasets that improve vision-language models, robotic perception, and autonomous decision-making. This position requires exceptional attention to detail, strong analytical and observational skills, and the ability to consistently apply detailed annotation guidelines with a high degree of accuracy.

Key responsibilities

• Review video clips and annotate visual content at specific timestamps
• Produce clear, accurate, and concise descriptions of actions, tasks, and events captured in each video
• Identify key moments in the video clips including critical actions, deviations, errors, or noteworthy behaviors, according to project guidelines
• Ensure consistency, accuracy, and efficiency while processing high volumes of data
• Adhere strictly to project guidelines, formatting, and quality standards

About you

• Strong attention to detail with the ability to identify subtle visual cues and differences
• Excellent observational, analytical, and visual interpretation skills
• Clear, concise, and professional written communication skills
• Ability to identify inconsistencies or issues and communicate them effectively
• Reliable, disciplined, and committed to producing high-quality work
• Ability to adapt swiftly and effectively in a dynamic environment
• Willingness to work on-site and provide cross-functional support to other teams as operational needs arise

About us

Eastworlds accelerates physical AI deployment through teleoperation and data collection, helping advanced robots move from the lab into real-world operations. We operate leading-edge robotic systems in structured real-world environments, generating the operational experience and training data needed to improve robot performance, reliability, and readiness for deployment.