Multimodal Mechanics

AI Trainer Jobs; United States; Remote; USD 45,000 - 65,000; Part time

Job summary

AuraOne is seeking a remote contractor for Multimodal Mechanics evaluation. You will review prompts and model outputs against a quality rubric, label edge cases, and provide structured feedback to retrain the system.

You will compare responses, tag issues, and maintain calibration against gold standards on a weekly cadence.

Qualifications

• Prior evaluation, annotation, or human-rater experience on multimodal mechanics evaluation or adjacent content for Multimodal Mechanics work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Reli able async availability for at least 10 hours per week.

Responsibilities

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

Skills

Model evaluation

Rubric annotation

Severity tagging

Inter-rater calibration

Multimodal evaluation

Job description

Multimodal Mechanics is a remote evaluation track for reviewing multimodal mechanics 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: Multimodal Evaluation · Pay: $40 / hr · Location: Remote — US-eligible · Contractor

Multimodal Mechanics is a remote evaluation track for reviewing multimodal mechanics 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.

Role details

About the role

Multimodal Mechanics is a remote evaluation track for reviewing multimodal mechanics evaluation prompts and responses against AuraOne's quality rubric.
AI data reviewers help turn multimodal mechanics evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Judge model behavior on text, images, and documents. Also on mixed-input tasks.

Responsibilities

• Evaluate multimodal mechanics evaluation model outputs against a versioned rubric and assign severity tags for Multimodal Mechanics 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 multimodal mechanics evaluation or adjacent content for Multimodal Mechanics 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.

Role signals

Example tasks

• Compare two multimodal mechanics 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

$40 / 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
• Multimodal Mechanics evaluation