AI Data QA & Labeling Specialist (Remote)

prolificacademicltd; United States; Remote; USD 21,000 - 34,000; Part time

Job summary

Prolific Academic Ltd. is seeking individuals to contribute database and records expertise to AI system training and evaluation. After a short skills assessment, successful applicants may review generated records, label data, and assess how accurately systems handle structured and messy inputs.

You will check AI-generated entries for accuracy, create realistic examples, identify errors in categorization and field-mapping, and classify samples against data quality rules.

Qualifications

• Experience in high-volume data entry, data processing, database administration, or records management.
• Careful attention to detail when finding duplicates, inconsistencies, and formatting problems.
• Ability to process structured and unstructured information efficiently without sacrificing accuracy.
• Familiarity with data formats and validation rules, plus the judgment to spot factual or logical errors.
• Written English sufficient to assess clarity and correctness; a payment account is required.

Responsibilities

• Check AI-generated entries and extracted records for accuracy, completeness, and consistent formatting.
• Create realistic examples involving ambiguous fields, conflicting information, or imperfect source data.
• Identify categorization, labeling, and field-mapping errors in generated datasets.
• Classify samples and compare outputs with data quality rules and professional standards.

Skills

Data entry

Data processing

Records management

Tools

Excel

Google Sheets

Airtable

SQL

Access

Job description

Prolific Academic Ltd. is seeking individuals to contribute database and records expertise to AI system training and evaluation. After a short skills assessment, successful applicants may review generated records, label data, and assess how accurately systems handle structured and messy inputs.

You will check AI-generated entries for accuracy, create realistic examples, identify errors in categorization and field-mapping, and classify samples against data quality rules.