Database Administrator

Database Administrator - Graduates - AI Training 

About Prolific

Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world.

Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.

The role

We're looking for Database Administrator to join our Expert Network to help train and evaluate cutting-edge AI models using real data expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models.

Researchers looking for your skills tend to pay up to $25 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.

What you’ll bring

• Professional Experience: years of experience in high-volume data entry, data processing, database management, or records administration.
• Accuracy & Attention to Detail: a proven track record of maintaining high accuracy rates across large datasets, with a sharp eye for inconsistencies, duplicates, and formatting errors.
• Speed & Efficiency: high typing speed and the ability to process structured and unstructured data quickly without sacrificing quality.
• Data Literacy: familiarity with data formats, validation rules, and the ability to identify when AI-generated outputs contain logical or factual errors.
• Communication Skills: solid written English skills sufficient to assess clarity and correctness in AI-generated text.
• Language Proficiency: multilingual capabilities are a significant plus, especially for evaluating data quality across localized datasets.
• A PayPal account to receive payment from our clients

What you’ll be doing in the role

• Evaluate AI Data Outputs: review AI-generated data entries, extractions, and structured records for accuracy, completeness, and formatting consistency.
• Simulate Data Entry Tasks: create realistic data entry scenarios and edge cases to test how AI handles messy inputs, ambiguous fields, or conflicting records.
• Audit AI-Generated Datasets: review AI-produced data for errors in categorisation, labelling, or field mapping, and flag issues against standard data quality rubrics.