Computer Vision MLE

AI Trainer Jobs; United States; Remote; USD 158,000 - 214,000; Full time

Job summary

AI Trainer Jobs is seeking a remote evaluator for Computer Vision MLE tasks. Review image/video outputs, judge spatial reasoning, and provide structured feedback to retrain models.

Responsibilities include rubric-based evaluation, severity tagging, and inter-rater calibration. Strong attention to detail and a reliable 10-hour weekly async availability are required.

Qualifications

• Prior evaluation, annotation, or human-rater experience in computer vision MLE or adjacent content.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause.
• Strong attention to detail and ability to flag prompt problems.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route for rubric updates.
• Calibrate reviewer quality scores against gold-standard examples weekly.

Skills

Model evaluation

Rubric annotation

Severity tagging

Inter-rater calibration

CV MLE evaluation

Job description

Computer Vision MLE is a remote evaluation track for reviewing computer vision mle evaluation prompts and responses against AuraOne's quality rubric.

Category: Video, Vision & Spatial AI · Pay: $135 / hr · Location: Remote — US-eligible · Contractor

Role details

About the role

Computer Vision MLE is a remote evaluation track for reviewing computer vision mle 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 computer vision mle 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 computer vision mle evaluation model outputs against a versioned rubric and assign severity tags for Computer Vision MLE 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on computer vision mle evaluation or adjacent content for Computer Vision MLE 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 computer vision mle 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

$135 / hr
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
• Computer Vision MLE evaluation