Computer Vision MLE

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

Category:
Video, Vision & Spatial AI
· Pay: $135 / hr
·

Location:

Remote — US-eligible
· Contractor

Computer Vision MLE is a remote evaluation track for reviewing computer vision mle evaluation prompts and responses against Aura One'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 Aura One Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

• 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.

• 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.

• 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.

• 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.

$135 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Computer Vision MLE evaluation