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You will source, curate, and validate datasets to improve AI system quality while generating synthetic examples. Additionally, you will review annotations, ensure consistency through audits, and refine labeling guidelines.
Join a network of data operations specialists supporting human-data projects for AI research. You will source, curate, and validate datasets that help improve the quality of AI systems, with projects matched to your background.
Source and collect documents, images, audio, or video according to project specifications, including capturing new media when needed.
Sort, de-duplicate, tag, and filter datasets against quality standards.
Generate synthetic examples to specification and validate them against labeling guidelines.
Review and adjudicate annotations, identifying work as correct, incorrect, or unclear.
Run calibration and audit rounds to support consistency across annotators.
Help write and refine labeling guidelines with clear, practical examples.
Under five years of hands-on experience in data operations, annotation quality assurance, dataset work, or a related detail-focused role.
Experience in data, quality assurance, research assistance, operations, or content review, or a degree in a data-related field such as statistics, linguistics, information science, computer science, library science, or GIS.
Experience sourcing, collecting, cleaning, or curating datasets and checking work against written guidelines or specifications.
Comfortable using Excel or Google Sheets and web-based tools.
Experience with annotation or data platforms such as Label Studio, Labelbox, CVAT, Scale, Remotasks, or Appen is a plus.
AI data project experience, including labeling, evaluations, synthetic data, or guideline writing, is valued. Additional language fluency is also a plus.
Strong attention to detail and clear written English.
Compensation is $20 to $40 per hour, depending on project, field, and experience level. Payment is for hours worked.
Fully remote. Projects may last from one week to several months.
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