Remote Handwriting QA & Data Quality Specialist

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Hourly compensation: $80–$120/hr

Job summary

AI Trainer Jobs is seeking a Handwriting Recognition Dataset QA Specialist for a remote contractor track. Review prompts and responses against AuraOne's quality rubric, label edge cases, and provide structured feedback to support model retraining.

You will compare paired outputs, identify edge cases, and help design and audit label taxonomies and evaluation datasets, with a focus on reliability and clear rationale.

Qualifications

• Prior evaluation, annotation, or human-rater experience on handwriting recognition dataset QA evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate handwriting recognition dataset QA evaluation model outputs against a versioned rubric and assign severity tags.
• 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.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Handwriting Recognition Dataset QA/QA

Multimodal evaluation

Cross-modal reasoning

Grounding review

Handwriting

Recognition

Education

Experience in linguistics, content moderation, or trust & safety review

Job description

AI Trainer Jobs is seeking a Handwriting Recognition Dataset QA Specialist for a remote contractor track. Review prompts and responses against AuraOne's quality rubric, label edge cases, and provide structured feedback to support model retraining.

You will compare paired outputs, identify edge cases, and help design and audit label taxonomies and evaluation datasets, with a focus on reliability and clear rationale.