Data Annotator

$6–10/hrrate as listed

Label and quality-check video and audio samples against detailed client guidelines for micro1's AI training projects. Observed pay runs $6–10/hr, with volume tied to individual client projects rather than a fixed weekly schedule.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

This is production annotation work on video and audio data. You'll be given a project-specific guideline document — often long, occasionally ambiguous — and asked to apply it consistently across hundreds of samples: segmenting clips, tagging speakers or events, transcribing or verifying transcripts, and marking attributes defined by the client's schema. A meaningful share of the queue is review work, where you audit annotations another contributor already made and correct or confirm them.

The part that separates strong annotators from average ones is edge-case handling. When a sample doesn't cleanly fit the guideline, the expectation is that you flag it with a written rationale rather than guessing silently. Project coordinators use those flags to tighten the spec, so clear documentation is part of the deliverable, not overhead.

What the platform screens for

micro1 runs an AI-led interview before matching you to a project. It probes:

Whether you can restate a complicated instruction accurately and identify where it's underspecified
Concrete examples of past detail-heavy work — annotation, transcription, QA, moderation, coding, or research
Your reasoning on ambiguous cases, including whether you default to flagging or to inventing a rule
English comprehension and written clarity, since guidelines and escalations are text-based
Realistic availability, and whether you can hold consistency across long sessions

No prior AI experience is required. Familiarity with labeling tools helps but is not a gate.

Logistics

Fully remote and asynchronous, contractor engagement. Work is delivered against milestones rather than fixed shifts, though projects typically expect a stated weekly commitment and can involve throughput or accuracy quotas. Pay is observed in the $6–10/hr band and varies by project, locale, and task complexity — treat it as reported, not guaranteed. Project length ranges from short bursts of a few weeks to ongoing queues; there is no promise of continuous volume between projects.

What they look for

Data annotation
Attention to detail

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have a stable internet connection and a suitable workspace from which you can work remotely for the duration of this project?
Remote work capability · required
Are you proficient in written and verbal English, able to read detailed annotation guidelines and communicate with project coordinators?
English proficiency · required
Do you have prior experience annotating, labeling, or otherwise categorizing data (audio, video, text, or images)?
Prior annotation or data labeling experience · preferred
Do you have previous experience specifically with annotating video or audio content?
Audio or video annotation background · preferred

0 of 4 answered

Included with this listing

Interview prep — 12 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate has hands-on annotation experience and can articulate a concrete workflow; depth of prior labeling exposure matters because it predicts speed and accuracy ramp-up.
A strong answer: The answer names a specific domain (medical imagery, transcripts, video frames, etc.), describes the annotation task in detail (e.g., 'marking objects in video frames' or 'labeling sentiment in reviews'), and explains a concrete quality-control step they took—spot-checking their own work, cross-referencing guidelines, or tracking decision rationale.
Where people lose it: Claiming annotation experience but describing only tangential work like 'reviewing documents' or 'managing databases'—or naming experience but then unable to explain the actual annotation task or how they maintained quality.