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.
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