OCR and Document Understanding Model Evaluator

AI Trainer Jobs; United States; Remote; USD 76,000 - 90,000; Full time

Job summary

AuraOne is seeking an OCR and Document Understanding Model Evaluator for a remote evaluation track. Reviewers assess document annotation prompts and responses against a quality rubric, labeling edge cases and providing structured feedback for retraining.

You will compare paired outputs, judge model behavior on text, images, and documents, and maintain calibration against gold standards each week. This is a contractor, remote role with US eligibility.

Qualifications

• Experience evaluating document annotations or related OCR/Document Understanding tasks.
• Ability to apply rubrics consistently across long batches.
• Strong written reasoning; clearly name issues and rubric clauses.
• Attention to detail and ability to flag problematic prompts.
• Comfort working asynchronously with at least 10 hours per week.

Responsibilities

• Evaluate document annotation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them for rubric updates.
• Maintain reviewer-quality scores by regular calibration against gold-standard examples.

Job description

OCR and Document Understanding Model Evaluator is a remote evaluation track for reviewing document annotation 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

OCR and Document Understanding Model Evaluator is a remote evaluation track for reviewing document annotation prompts and responses against AuraOne's quality rubric.

About the role

OCR and Document Understanding Model Evaluator is a remote evaluation track for reviewing document annotation 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 document annotation 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 document annotation model outputs against a versioned rubric and assign severity tags for OCR and Document Understanding 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.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

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 document annotation or adjacent content for OCR and Document Understanding 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.

Role signals

Example tasks

• Compare two document annotation 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
• Document annotation
• Multimodal evaluation
• Cross-modal reasoning
• Grounding review
• Document
• Understanding