Cross-Modal Retrieval Model Evaluator

AI Trainer Jobs; United States; Remote; USD 47,000 - 63,000; Full time

Job summary

AuraOne is seeking a Cross-Modal Retrieval Model Evaluator for a remote evaluation track reviewing prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and write structured feedback to help retrain the model.

As an independent contractor, you will review text, images, and documents, flag issues, and provide clear rationales. Availability around 10 hours per week, pay $40/hr, rate confirmed after interview.

Qualifications

• Prior evaluation, annotation, or human-rater experience on cross modal retrieval evaluation.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause.
• Strong attention to detail and flag when prompts are problematic.
• 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 the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route to the program team for rubric updates.

Skills

Cross-modal evaluation

Annotation experience

Structured feedback writing

Attention to detail

Independent contractor work

Job description

Cross-Modal Retrieval Model Evaluator is a remote evaluation track for reviewing cross modal retrieval model 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

Cross-Modal Retrieval Model Evaluator is a remote evaluation track for reviewing cross modal retrieval model evaluation prompts and responses against AuraOne's quality rubric.

About the role

Cross-Modal Retrieval Model Evaluator is a remote evaluation track for reviewing cross modal retrieval model evaluation prompts and responses against AuraOne's quality rubric.
AI data reviewers help turn cross modal retrieval model 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 cross modal retrieval model evaluation model outputs against a versioned rubric and assign severity tags for Cross-Modal Retrieval Model Evaluator 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.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on cross modal retrieval model evaluation or adjacent content for Cross-Modal Retrieval Model Evaluator 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 cross modal retrieval model 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

Hourly rate confirmed after the interview process.
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
• Cross Modal Retrieval Model evaluation
• Multimodal evaluation
• Cross-modal reasoning
• Grounding review
• Cross
• Modal
• Retrieval

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.

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

Placement timing depends on program demand and reviewer confirmation.