Mercor
MercorVerified Source
Remote

AI Data Generalist — Egocentric Video Review | $20-$25/hr Remote

20–25/hr
Remote
Posted August 14, 2026
hourly
5 openings

Overview

A remote, short-term role for detail-oriented researchers to review egocentric video data. You’ll help validate action segment boundaries across multiple brief projects, using project-specific guidelines and precise judgment. The position leverages careful observation, structured review work, and clear written communication to ensure high-quality annotations in first-person video data. This opportunity runs about two weeks, with three compact projects starting mid August.

What You'll Do5

  • 1Assess action segment boundaries in egocentric videos and verify alignment with performed actions
  • 2Adjust or flag boundary discrepancies according to the provided criteria to maintain accuracy
  • 3Maintain consistency across a large set of short action segments and meet project deadlines
  • 4Document findings clearly and follow project instructions for each dataset
  • 5Support additional related data review tasks as new projects begin during the engagement

Requirements5

  • 1Strong attention to visual detail and ability to differentiate similar actions
  • 2Comfort with repetitive, structured review while preserving accuracy
  • 3Ability to quickly learn and apply annotation guidelines in written form
  • 4Solid English communication skills for documenting results
  • 5Previous experience with video annotation or computer vision datasets is a plus but not required

Who Should Apply

Ideal candidates are meticulous, patient reviewers who can handle routine tasks with careful consistency. If you enjoy following clear guidelines, spotting subtle differences in video actions, and delivering precise annotations on a tight schedule, this role fits you well.

Salary Insight

Pay range is $20.00 to $25.00 per hour for remote, hourly work.

Location

Typeremote
LocationRemote
This is a remote position

Required Skills

egocentric video annotationquality reviewdata labelingstructured writing

Application Tip

Read each project guideline at the start, note one key boundary rule, and apply it consistently across the entire dataset to establish a reliable baseline.

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