Data Labeling Associate - Video Content

AI Trainer Jobs; United States; Remote; USD 21,000 - 48,000; Full time

Job summary

AuraOne is seeking a Data Labeling Associate - Video Content to remotely review video prompts and responses against our quality rubric, compare outputs, and label edge cases for retraining. You will evaluate model outputs, judge spatial reasoning, and document structured feedback.

This is a contractor role, remote in the US, with hourly compensation after the interview. Ideal candidates have data-labeling or annotation experience, strong attention to detail, and the ability to work

Qualifications

• Prior evaluation or annotation experience is preferred.
• Strong attention to detail and ability to apply rubrics consistently.
• Clear written reasoning that identifies issues and rubric clauses.
• Comfort working asynchronously with limited supervision.

Responsibilities

• Evaluate data labeling video content outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route them for rubric updates.

Skills

Data labeling

Content moderation

Inter-rater agreement

Linguistics

Job description

Data Labeling Associate - Video Content is a remote evaluation track for reviewing data labeling video content evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Category: Video, Vision & Spatial AI · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Data Labeling Associate - Video Content is a remote evaluation track for reviewing data labeling video content evaluation prompts and responses against AuraOne's quality rubric.

About the role

Data Labeling Associate - Video Content is a remote evaluation track for reviewing data labeling video content evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn data labeling video content evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

Responsibilities

• Evaluate data labeling video content evaluation model outputs against a versioned rubric and assign severity tags for Data Labeling Associate - Video Content assignments.


• Compare paired responses and pick the stronger answer with a written rationale.


• Label hallucinations, instruction-following failures, and unsafe content with structured tags.


• Capture ambiguous prompts and route them back to the program team for rubric updates.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on data labeling video content evaluation or adjacent content for Data Labeling Associate - Video Content work.


• Comfort applying multi-page rubrics consistently across long batches.


• Clear written reasoning that names the issue and the rubric clause being applied.


• Strong attention to detail and the ability to flag when a prompt itself is the problem.


• Reliable async availability for at least 10 hours per week.

Example tasks

• Compare two data labeling video content evaluation model responses to the same prompt and pick the stronger one with rationale.


• Tag an unsafe response with the correct policy category and severity.


• Audit a 50-row batch for rubric consistency and report drift to the program lead.


• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.


• Experience with inter-rater agreement metrics and calibration cycles.


• Domain expertise that lets you spot subject-matter errors automated checks miss.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Model output evaluation


• Rubric-based annotation


• Severity tagging


• Inter-rater calibration


• Data Labeling Video Content evaluation


• Design

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.
- 01 Confirm profile and eligibility details.
- 02 Attach this role deliberately.
- 03 Receive a human review decision or follow-up.
Placement timing depends on program demand and reviewer confirmation.