Remote Computer Vision Data Labeler & Rubric Evaluator

AuraOne, Inc.; United States; Remote; USD 28,000 - 55,000; Full time

Job summary

AuraOne, Inc. is seeking a remote Computer Vision Data Annotation Specialist contractor to evaluate model outputs against a versioned rubric, tag edge cases, and provide structured feedback for retraining.

You will compare paired responses, label hallucinations and safety issues, and calibrate against gold standards to maintain reviewer-quality scores. Strong attention to detail and clear reasoning are essential.

Qualifications

• Prior evaluation, annotation, or human-rater experience on computer vision data annotation evaluation or adjacent content.
• Ability to apply 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.

Responsibilities

• Evaluate computer vision data annotation evaluation model outputs against a versioned rubric and assign severity tags for 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.
• 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

Computer Vision Data Annotation eval

Job description

AuraOne, Inc. is seeking a remote Computer Vision Data Annotation Specialist contractor to evaluate model outputs against a versioned rubric, tag edge cases, and provide structured feedback for retraining.

You will compare paired responses, label hallucinations and safety issues, and calibrate against gold standards to maintain reviewer-quality scores. Strong attention to detail and clear reasoning are essential.