Generalist for Data Annotation

$21–35/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Worldwide
Field: Generalist

Annotate and categorize image data, then review labels for consistency against detailed project guidelines. The work is remote and includes documenting decisions, resolving ambiguous cases, and adapting to revised standards.

The work

This contractor role focuses on image data annotation for an AI training project. Day to day, you will label and categorize images, validate annotations, correct discrepancies, and meet defined project timelines. You will also document labeling methods and explain the rationale behind decisions.

What the screen is likely to assess

The screening will look for specific evidence that you can follow detailed taxonomies, use image annotation tools, and make consistent decisions in ambiguous cases. Expect follow-up questions about quality-control methods, prior image-based work, handling guideline changes, and how you distinguish a true annotation error from a reasonable interpretation. Prior AI experience is not required, while data annotation experience is preferred and a background in computer vision, data analysis, or a related domain is a plus.

Practical details

The role is remote and involves collaboration with a distributed project team, including written and verbal clarification with participants and stakeholders. The listing does not specify typical weekly hours, working windows, or whether all work is asynchronous, so candidates should be prepared to state their realistic availability and ask about turnaround expectations.

Pay band: $21–35/hr

What they look for

image data annotation
attention to detail
written and verbal communication
remote collaboration
annotation tools
quality assurance
documentation
adaptability
time management

Included with this listing

Interview prep — 10 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: This tests whether your claimed experience covers hands-on image labeling, review, taxonomy use, or quality control rather than adjacent data work.
A strong answer: Describe a specific dataset, the annotation types involved, the tool used, your responsibilities, and how your output was checked. If you lack formal annotation experience, use a comparable task involving rule-based image classification or structured review and state the limitation plainly.
Where people lose it: Candidates lose points by saying they have labeled data without identifying the image task, label format, guidelines, or personal contribution.

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