AI Video Annotator

Welo Data; Malaysia; Remote; MYR 17,000 - 28,000; Full time

Job summary

Welo Data is seeking detail-oriented annotators to label robot manipulation videos for AI training. You will watch short videos from three synchronized cameras and produce precise, natural-language descriptions of the actions, following a detailed style guide.

Responsibilities include segmenting videos, applying multi-level labels, ensuring full coverage of every moment, and reporting any unexpected outcomes. This remote role offers flexible hours; prior annotation experience is a plus.

Qualifications

• Strong written English; produce precise descriptive sentences per video.
• Ability to follow a detailed, structured style guide consistently.
• Experience with video annotation or QA is a plus.

Responsibilities

• Watch robot manipulation videos from three cameras and write clear segment descriptions.
• Apply labels at atomic, skill, and task levels for each segment.
• Cross-reference all three camera views to confirm details and object identities.
• Describe any deviations, failures, or unusual events with precision.
• Ensure every moment is labeled at two or more levels and no gaps.

Skills

Strong written English

Attention to detail

Style-guide discipline

Spatial description

Independent work

Tools

Label Studio

Job description

We are looking for detail-oriented annotators to help label robot manipulation videos for AI training purposes.You'llwatch short videos of robots performing manipulation tasks (filmed from three synchronized camera angles) and produce precise, structured, natural-language descriptions of the actions taking place. This work directly supports the development of robotics AI models and requires strong written English, sharp observational skills, and the discipline to follow a detailed style guide consistently.

· Location: Remote in India/Philippines/Mexico/Egypt/Indonesia/Malaysia/Brazil

What You'll Do

1. Watch short robot manipulation videos, each filmed from three synchronized camera views (an overhead view and views from each of the robot's two wrist-mounted cameras).
2. Break each video into time segments and write clear, natural-language descriptions for each segment.
3. Apply labels at three levels of detail for each applicable segment:
   • - Atomic motion(a few seconds) — a single small movement (e.g., "close fingers around the red handle")
   • - Skill / subtask(several seconds to ~20seconds) —a complete, meaningful action (e.g., "pick up the red block by its edge")
   • - Task / goal(up to ~1minute) —the overall purpose of a sequence of skills (e.g., "place all blocks in the container")
4. Ensure every moment of video is covered by a label at two or more of these levels — no gaps, including idle or pause moments.
5. Accurately describe exactly what happens, including when something doesn't go as planned (a dropped object, a failed grasp, a slipped grip). Precision matters more than making the robot look successful.
6. Cross-reference all three camera angles: use the overhead view to understand the overall scene and object identity, and the close-up wrist views to confirm exact contact and grasp details.
7. Follow a detailed style guide covering vocabulary for actions, spatial relationships, object descriptions, and manner of movement, applying it consistently across many episodes.
8. Participate in periodic calibration sessions to align your labeling with the team and the client's reference examples.

What We're Looking For

Required:

• 1. Strong written English — you'll write dozens of short, precise descriptive sentences per video and need to vary your language rather than repeating the same phrases.
• 2. Sharp attention to detail — able to distinguish small differences (a successful grasp vs. a fumble, a push vs. a drag, which specific object part is being touched).
• 3. Comfort following a detailed, structured style guide and applying it consistently, even in ambiguous or edge-case scenarios.
• 4. Basic comfort with spatial/mechanical description (left/right, above/below, naming object parts like handles, lids, or edges).
• 5. Reliable, self-directed work habits — this is often heads-down work with periodic check-ins rather than close supervision.

Nice to Have:

• 1. Prior experience with video annotation, data labeling, transcription, or QA work.
• 2. Familiarity with robotics terminology (grippers, end-effectors, manipulation) — helpful but not necessary, as the style guide is self-contained.
• 3. Experience with annotation tools such as Label Studio.

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