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Team Lead, Human Data Operations

Pubblicato il 12-10-2026 - Jobgether in Italia

This is a hands-on leadership opportunity focused on building high-quality human data for advanced AI systems. You will lead and develop a team of AI Tutors responsible for labeling, reviewing, and evaluating visual data across image and video domains. You will own quality and delivery, establish strong operational standards, and ensure that project guidelines are consistently followed. The role combines people leadership with direct involvement in data review and quality assurance. Working closely with data, engineering, and operations teams, you will help translate AI model requirements into effective data strategies.
Own end-to-end quality and delivery for assigned human data projects, personally reviewing work to ensure accuracy, consistency, guideline adherence, and high-quality output at scale.
Lead, coach, and performance-manage a team of AI Tutors through regular feedback, performance reviews, action plans, shadow sessions, and continuous development.
Actively participate in labeling and review activities to maintain quality standards and demonstrate best practices.




Manage guideline adherence, taxonomy processes, quality assurance workflows, and certification standards.
Track team and project performance through KPIs and dashboards, including quality metrics, throughput, and send-back rates.
Develop, maintain, and deliver training materials, practice exercises, and certification benchmarks; oversee certifications and workforce adjustments.
Partner with Human Data Managers, other Team Leads, and Engineering to translate AI model requirements into clear labeling strategies and operational guidelines.
Foster a high-performance culture by developing talent, maintaining accountability, and supporting performance or disciplinary processes when required.
Document project outcomes, risks, and results, recommend process improvements, and provide regular status updates.
Represent the needs and perspectives of AI Tutors while promoting collaboration across the wider Hu

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