In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Responsibilities
Review video annotations for accuracy, consistency, and completeness across diverse datasets.
Identify and tag quality issues or discrepancies in action annotations and hand-pose data.
Make precise and thoughtful corrections to improve overall annotation quality.
Evaluate English-language action annotations, ensuring they meet established guidelines.
Assess and validate hand-pose data as needed, referencing detailed quality standards.
Provide concise written feedback on annotation issues and contribute suggestions for guideline improvements.
Document findings, corrections, and recommendations in standardized formats.
Preferred skills
Exceptional attention to detail and a methodical approach to reviewing video data.
Comfortable working with video annotation tools and large-scale video datasets.
Strong proficiency in English reading and writing, especially for assignments involving action-annotation review.
Clear and effective written and verbal communication skills for reporting issues or providing feedback.
Ability to follow detailed quality guidelines and project instructions consistently.
Background or familiarity with action recognition, hand-pose analysis, or related annotation projects is a plus.
Self-motivated, organized, and able to manage deliverables independently within the agreed project scope.
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.