Remote AI Data Labeler: Rubric-Based Evaluator

AuraOne; United States; On-site; USD 13,776 - 20,664; Full time

Benefits offered by this job

Remote work

Job summary

AuraOne is seeking a detail-oriented Household Activity Video Contributor (US Based) to remotely review model outputs against our quality rubric. You will compare paired responses, label edge cases, and write structured feedback that helps retrain our models.

You should have prior experience in evaluation or annotation, be comfortable applying long rubrics across large batches, and provide clear, written rationales naming rubric clauses.

Qualifications

• Prior evaluation, annotation, or human-rat or experience on household activity video contributor evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate household activity video contributor evaluation model outputs against a versioned rubric and assign severity tags.
• 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.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.
• Document recurring failure modes so the modeling team can target them in the next training run.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Household Activity Video Contributor

Eligibility

Job description

AuraOne is seeking a detail-oriented Household Activity Video Contributor (US Based) to remotely review model outputs against our quality rubric. You will compare paired responses, label edge cases, and write structured feedback that helps retrain our models.

You should have prior experience in evaluation or annotation, be comfortable applying long rubrics across large batches, and provide clear, written rationales naming rubric clauses.