Team Lead, Human Data Operations - Post Training

Accountabilities

• Own end-to-end quality and delivery for assigned Human Data projects, personally reviewing domain-specific work and ensuring accuracy, consistency, guideline adherence, and high-quality data production at scale.
• Lead, coach, and performance-manage a team of AI Tutors through regular reviews, documented feedback, action plans, shadow sessions, and continuous development.
• Participate directly in labeling, annotation, and review activities to maintain quality standards and demonstrate best practices.
• Establish and maintain rigorous guideline adherence, taxonomy management, and quality assurance processes.
• Monitor team and project performance through KPIs such as quality scores, throughput, send-back rates, and other operational metrics.
• Identify operational bottlenecks and implement process improvements to increase efficiency, quality, and scalability.
• Create, maintain, and deliver training materials, practice exercises, and certification benchmark tasks.
• Manage certification processes and make workforce adjustments based on performance, project needs, and operational requirements.
• Collaborate with Human Data Managers, other Team Leads, and Engineering teams to translate model requirements into clear labeling strategies and operational guidelines.
• Develop team capabilities while maintaining strong accountability and a high-performance culture, including supporting disciplinary processes when necessary.
• Document project outcomes, identify opportunities for process iteration, and communicate project status, risks, and results to relevant stakeholders.
• Represent the needs and perspectives of AI Tutors while fostering effective collaboration across the wider Human Data organization.
• Balance strategic thinking with hands-on execution in a fast-moving environment where priorities may evolve quickly.

Requirements:

• At least 1 year of hands-on experience in data labeling, annotation, AI training or evaluation, content quality, or a similar operational environment.
• Bachelor’s degree or at least 4 years of relevant professional experience in place of a degree.
• Demonstrated understanding of data quality metrics, annotation processes, and guideline-driven workflows.
• Basic understanding of artificial intelligence and machine learning concepts, particularly the relationship between training data quality and model performance.
• Familiarity with project management and collaboration platforms such as Notion, Axiom, JIRA, Linear, or equivalent tools.
• Previous experience leading or mentoring small teams in data labeling, annotation, content quality, or related operations is highly valued.
• Strong domain expertise in at least one relevant field, such as design, psychology, philosophy, writing, or another humanities discipline, is preferred.
• Proven ability to manage multiple concurrent projects and prioritize effectively in a fast-paced environment.
• Strong experience reviewing domain-specific work, maintaining data quality, and improving operational processes.
• Experience with coaching, performance management, training development, and certification programs is an advantage.
• Strong analytical capabilities and the ability to use data and KPIs to identify trends and drive continuous improvement; SQL knowledge is a plus.
• Experience managing distributed teams is desirable.
• Excellent written and verbal communication skills, with the ability to build rapport, communicate clearly, and align diverse stakeholders.
• Exceptional organizational skills and attention to detail.
• Proactive and structured approach to problem-solving, with a strong focus on continuous improvement.
• Ability to combine strategic thinking with hands-on execution.
• Strong interest in developing high-performing teams and scaling reliable, high-quality Human Data operations.
• Fluent English communication skills.
• Ability to work effectively in a highly collaborative, flat, and fast-moving environment where initiative and ownership are expected.

Benefits:

• Fully remote working opportunity, subject to applicable employment and location requirements.
• Competitive compensation, with international salary details shared during the recruitment process.
• Equity participation as part of the overall rewards package, where applicable.
• Comprehensive medical, dental, and vision coverage for eligible employees, depending on location and employment type.
• Access to retirement benefits such as a 401(k) plan for eligible U.S.-based positions.
• Short- and long-term disability insurance and life insurance for eligible employees.
• Paid sick leave and additional benefits depending on location and jurisdiction.
• Various employee discounts, perks, and additional rewards.
• Opportunity to work at the intersection of AI, human data, and advanced machine learning systems.
• Direct impact on the quality of data used to train and evaluate next-generation AI models.
• Opportunity to lead and develop a high-performing distributed team.
• Hands-on exposure to complex AI training and evaluation operations.
• Fast-paced environment with significant opportunities to contribute ideas, improve processes, and take ownership.
• International and collaborative working environment.