Image-Text Grounding Dataset QA Specialist

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

Benefits offered by this job

Remote work

Contractor role

Hourly pay after interview

Job summary

AuraOne is seeking an Image-Text Grounding Dataset QA Specialist for a remote evaluation track. You will review prompts and responses against our quality rubric, compare paired outputs, label edge cases, and provide structured feedback to help retrain the model.

This contractor role requires prior evaluation or annotation experience, strong attention to detail, and clear written reasoning. Expected ~10 hours per week, US-eligible.

Qualifications

• Experience with evaluation, annotation, or human-rater work on image-text grounding datasets.
• Clear written reasoning naming issues and rubric clauses.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them for rubric updates.

Job description

Image-Text Grounding Dataset QA Specialist is a remote evaluation track for reviewing image text grounding dataset qa 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

Image-Text Grounding Dataset QA Specialist is a remote evaluation track for reviewing image text grounding dataset qa evaluation prompts and responses against AuraOne's quality rubric.

About the role

Image-Text Grounding Dataset QA Specialist is a remote evaluation track for reviewing image text grounding dataset qa 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 image text grounding dataset qa 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 image text grounding dataset qa evaluation model outputs against a versioned rubric and assign severity tags for Image-Text Grounding Dataset QA Specialist 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 image text grounding dataset qa evaluation or adjacent content for Image-Text Grounding Dataset QA Specialist 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 image text grounding dataset qa 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
• Image Text Grounding Dataset QA evaluation
• Multimodal evaluation
• Cross-modal reasoning
• Grounding review
• Image
• Text
• Grounding