Vision-Language-Action (VLA) Annotator
Location: Remote
Type: Full-time / Contract
About the role
We are looking for detail-oriented VLA Annotators to support the creation of high-quality training data for Vision-Language-Action and robotics models.
Annotators will review videos of people performing real-world activities, divide them into meaningful action segments, and create or validate clear descriptions of the actions taking place. The role also involves reviewing AI-generated annotations, correcting inaccuracies, and ensuring the final data meets project-specific quality standards.
Projects may include household, workplace, industrial, manipulation, navigation, and other real-world activities.
Responsibilities
Review videos and understand the overall task or activity being performed.
Divide videos into logical action-based segments with accurate start and end times.
Ensure all meaningful actions are captured without unnecessary gaps or overlaps.
Review AI/VLA-generated captions and select, edit, or replace them when necessary.
Write clear and concise action descriptions based only on what is visibly happening in the video.
Accurately describe relevant hands, objects, tools, movements, and locations when required.
Maintain consistent terminology for objects and actions throughout each annotation.
Identify invalid, idle, distracted, failed, recovery, or otherwise unusual portions of a video according to project guidelines.
Review completed annotations for segmentation, caption accuracy, grammar, consistency, and completeness before submission.
Follow project-specific annotation guidelines and adapt quickly when requirements change.
Flag ambiguous examples, recurring model errors, edge cases, or unclear instructions to the Operations/Quality team.
Use annotation shortcuts and AI-assisted tools efficiently while maintaining required quality and productivity targets.
Participate in calibration, training, and feedback sessions as needed.
Requirements
Strong attention to detail.
Ability to understand and consistently apply detailed written guidelines.
Good written English and the ability to describe actions clearly using simple, precise language.
Strong observational skills and ability to distinguish small differences in actions, objects, and timing.
Comfortable reviewing video content for extended periods.
Ability to perform repetitive annotation work while maintaining accuracy and consistency.
Basic computer proficiency and ability to learn new annotation platforms quickly.
Ability to work independently and meet defined quality and productivity targets.
Reliable computer and internet connection.
Openness to feedback and ability to quickly incorporate guideline changes.
Nice to have
Previous experience in data annotation, video annotation, quality assurance, robotics data, computer vision, or AI training data.
Experience working with temporal segmentation or action recognition tasks.
Familiarity with AI-assisted labeling tools or reviewing model-generated outputs.
Experience with egocentric / first-person video data.
Basic familiarity with robotics, manufacturing, household tasks, or other physical-world activities.
What success looks like
Accurate segmentation of real-world actions.
Clear, self-contained, and consistent captions.
Minimal missed actions, unnecessary segments, or timing errors.
Strong judgment when reviewing AI-generated annotations.
Consistent quality across large volumes of video.
Ability to identify recurring annotation or model issues rather than treating every task in isolation.
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