Database Administrator – Graduates – AI Training We are looking for a Database Administrator to join our Expert Network and help train and evaluate cutting‑edge AI models using real data expertise. Successful candidates will complete a brief assessment and, upon passing, will be invited to participate in paid tasks evaluating AI models. Researchers typically pay up to $25 per hour for tasks that may 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. Annotation & Labelling: tag and classify data samples to help AI models learn correct data structures, formats, and validation rules. Quality Assurance: compare AI outputs against established data entry standards to ensure they meet professional accuracy and consistency benchmarks. Key Technologies Data Tools: proficiency with Microsoft Excel, Google Sheets, or database platforms such as Airtable, SQL, or Access. Data Management Systems: experience with CRM platforms, ERP systems, or document management tools. Documentation: familiarity with Confluence, Notion, or similar platforms for referencing data standards and internal guidelines. #J-18808-Ljbffr