This is a remote, project-based role for experienced practitioners across any professional domain who can apply their real-world expertise to data annotation tasks.
You will review, label, and evaluate data samples — including text, documents, model outputs, and domain-specific content — providing expert judgment, structured annotations, and reasoned assessments that require genuine hands-on professional experience. This position offers exceptional pay, exposure to cutting-edge AI development workflows, and a strong addition to your professional portfolio.
Why apply
Flexible Time Commitment – Work on your schedule while contributing meaningful domain expertise.
Portfolio Building – Gain experience contributing expert knowledge to real-world AI and data projects.
Professional Growth – Apply your domain expertise in a novel, high-impact context at the frontier of AI development.
Responsibilities
Review and annotate data samples — including text, documents, and model-generated outputs — using structured labeling frameworks and annotation guidelines.
Apply domain-specific expertise to evaluate the accuracy, quality, and appropriateness of content requiring professional judgment.
Provide detailed reasoning, justifications, and source citations to support annotation decisions.
Flag ambiguous, inconsistent, or out-of-scope samples and escalate with clear written explanations.
Document annotation decisions consistently and reproducibly in line with project-specific guidelines.
Assessment requirements
One assessment, ongoing matches.
Schedule details
Flexible Time Commitment – Work on your schedule while contributing meaningful domain expertise.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.
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