Video Annotator
About this role
Video Annotator
Location: Fully Remote Schedule: Full-Time, Remote
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About the Role
We are looking for detail-oriented Video Annotators to support a Vision-Language-Action (VLA) dataset project. In this role, you will watch short video clips of physical tasks/activities and produce event-level annotations by segmenting each video into meaningful steps and writing clear, precise natural-language instructions describing the action taking place in each segment.
Key Responsibilities
Watch assigned videos in full before beginning annotation to understand the overall task and context.
Segment each video into discrete, logical steps/events based on visible action boundaries.
Write clear, concise, and grammatically correct step-by-step descriptions for each segment, accurately reflecting what is happening on screen.
Ensure annotations are action-oriented, unambiguous, and consistent in tense, tone, and phrasing across the dataset.
Assign accurate start and end timestamps for each segment.
Follow annotation guidelines, taxonomies, and style guides provided by the project team.
Flag videos that are unclear, corrupted, mislabeled, or otherwise unsuitable for annotation.
Participate in calibration sessions and incorporate reviewer/QA feedback to improve annotation quality and consistency.
Meet daily/weekly productivity and quality targets without compromising accuracy.
Maintain confidentiality of all project data and materials.
Required Qualifications
English Proficiency: C1 level (CEFR) or above — strong command of grammar, vocabulary, and sentence construction is essential.
Excellent attention to detail and ability to spot subtle changes in visual scenes.
Strong written communication skills; ability to describe actions concisely and precisely.
Comfort working with video-based tools and annotation software (training provided).
Ability to work independently, follow detailed guidelines, and maintain consistency over repetitive tasks.
Reliable internet connection and access to a computer with a modern browser.
Basic computer literacy (file handling, web-based tools, spreadsheets).
Preferred Qualifications
Prior experience in data annotation, labeling, transcription, or content moderation.
Familiarity with AI/ML concepts, especially computer vision, NLP, or robotics datasets.
Experience writing instructional or procedural content (how-to guides, SOPs, recipes).
Exposure to annotation platforms (e.g., CVAT, Label Studio, Scale, or similar tools).
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Originally posted on Himalayas
