AI Data QA & DB Administrator - Remote

Prolific; United States; Remote; USD 21,000 - 34,000; Full time

Job summary

Prolific is seeking a Database Administrator to join our Expert Network for AI training and evaluation using real data expertise. You may complete a brief test, and upon success you’ll join Prolific as a participant, with pay for AI tasks. Work may involve one-hour tasks or shorter, paid per hour at up to $25.

Your role focuses on evaluating AI data outputs, simulating data entry scenarios, auditing datasets, and tagging data samples to improve model learning and data quality.

Qualifications

• Experience with high-volume data entry or data processing.
• High accuracy rates across large datasets with attention to inconsistencies.
• Fast typing and ability to process structured/unstructured data efficiently.
• Familiarity with data formats, validation rules, and spotting AI-generated errors.
• Strong written English skills to assess clarity and correctness of text.
• Multilingual capabilities are a plus for evaluating localized data.

Responsibilities

• Evaluate AI data outputs for accuracy and formatting.
• Create realistic data entry scenarios to test AI handling of messy inputs.
• Audit AI-generated datasets and flag issues against data quality rubrics.
• Tag and classify data samples to support AI model learning of data structures.
• Ensure data quality by comparing outputs to established standards.

Skills

Professional experience

Accuracy & attention

Speed & efficiency

Data literacy

Communication skills

Language proficiency

PayPal account

Tools

Excel

Google Sheets

Airtable

SQL

Access

Job description

Prolific is seeking a Database Administrator to join our Expert Network for AI training and evaluation using real data expertise. You may complete a brief test, and upon success you’ll join Prolific as a participant, with pay for AI tasks. Work may involve one-hour tasks or shorter, paid per hour at up to $25.

Your role focuses on evaluating AI data outputs, simulating data entry scenarios, auditing datasets, and tagging data samples to improve model learning and data quality.