Multimodal Physics

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

Benefits offered by this job

Remote work

Contractor role

Flexible schedule

Job summary

AuraOne is seeking contractors to review multimodal physics evaluation prompts and responses. You will compare paired outputs, label edge cases, and provide structured feedback to help retrain models.

As a remote role with US-eligibility, you will judge model behavior on text, images, and documents, writing clear rationales and tagging issues for improvement. Expected commitment is flexible but requires reliable async availability.

Qualifications

• Prior evaluation, annotation, or human-rater experience in multimodal evaluation.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause applied.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and choose stronger answer 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

Evaluation experience

Rubric application

Attention to detail

Strong written reasoning

Async availability

Tools

Annotation tools

Job description

Multimodal Physics is a remote evaluation track for reviewing multimodal physics 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 Physics is a remote evaluation track for reviewing multimodal physics evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

Multimodal Physics is a remote evaluation track for reviewing multimodal physics 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 multimodal physics 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 physics evaluation model outputs against a versioned rubric and assign severity tags for Multimodal Physics 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 physics evaluation or adjacent content for Multimodal Physics 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 physics 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 Physics evaluation

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

1. 01 Confirm profile and eligibility details.
2. 02 Attach this role deliberately.
3. 03 Receive a human review decision or follow-up.

Placement timing depends on program demand and reviewer confirmation.