Expert Team Lead, Engineering

Accountabilities: Own end-to-end quality and delivery for assigned Human Data projects, personally reviewing engineering-domain work and ensuring accuracy, consistency, guideline adherence, and high-quality data production at scale. Lead, coach, and performance-manage a team of AI Tutors, including full-time employees and contractors, through regular reviews, action plans, shadow sessions, and continuous improvement initiatives. Oversee and actively participate in labeling and review activities to maintain high standards and demonstrate effective working practices. Ensure consistent adherence to project guidelines, taxonomies, and quality assurance processes. Identify operational bottlenecks and implement process improvements to increase efficiency, quality, and throughput. Monitor performance through KPI dashboards, using metrics such as quality, throughput, and send-back rates to guide improvements. Create, maintain, and deliver training materials, practice exercises, and certification benchmark tasks. Manage certification processes and make appropriate workforce adjustments based on performance and project requirements. Collaborate with Human Data Managers, other Team Leads, and Engineering teams to translate model requirements into clear labeling strategies and operational guidelines. Coach and develop team members while maintaining accountability and a high-performance culture, including supporting disciplinary processes when required. Document project outcomes, recommend process iterations, and communicate project status, risks, and results. Represent the needs and perspectives of AI Tutors while fostering collaboration across the wider Human Data organization. Requirements Bachelor's degree or higher in Mechanical, Electrical, Chemical, Civil, Environmental, or another closely related engineering discipline. At least 2 years of professional engineering experience in areas such as design, analysis, testing, research and development, or systems integration; alternatively, a Master's degree or higher in engineering or a related field. Basic understanding of artificial intelligence and machine learning concepts, including the role of high-quality training data in model performance. Advanced engineering degree such as an MS or PhD, or professional credentials such as PE or FE/EIT, is preferred. Demonstrated experience with data-quality metrics, annotation processes, and guideline-driven workflows. Breadth across multiple engineering disciplines, with the ability to credibly review technical work beyond your primary specialty. Experience with performance management, coaching, training program development, or certification frameworks. At least 1 year of hands-on experience in data labeling, annotation, AI training or evaluation, content quality, or a similar operational field is preferred. Experience developing scalable, high-performance technology applications is advantageous. Previous experience leading small teams in data labeling, annotation, or related operational environments is preferred. Proven ability to manage multiple concurrent projects, prioritize effectively, and deliver in a fast-paced environment. Strong track record of reviewing technical or domain-specific work, maintaining data quality, and improving operational processes. Strong analytical capabilities and experience using data and KPIs to drive continuous improvement; SQL knowledge is a plus. Experience managing distributed teams or mixed employment structures, including full-time employees and contractors. Excellent written and verbal communication skills, with the ability to build rapport quickly and align stakeholders. Strong organizational skills, attention to detail, proactive problem-solving abilities, and a continuous-improvement mindset. Ability to combine strategic thinking with hands-on execution. Strong domain expertise in at least one relevant area such as Finance, STEM, Coding/Software Engineering, or another technical field. Passion for developing high-performing teams and scaling reliable, high-quality Human Data operations. Benefits Competitive compensation, with international salary details provided during the recruitment process. For eligible U.S.-based positions, base salary ranges from $104,000 to $170,400 USD , depending on relevant experience, skills, education, geographic location, and qualifications. Equity opportunities as part of the overall rewards package. Comprehensive medical, dental, and vision coverage, subject to employment type, location, and jurisdiction. Access to a 401(k) retirement plan for eligible U.S.-based positions. Short- and long-term disability insurance. Life insurance. Paid sick leave for eligible U.S.-based positions. Additional employee discounts and perks. Remote working options where permitted based on location and employment arrangements. Opportunity to work at the intersection of engineering, human data, and advanced AI development. Collaborative environment emphasizing autonomy, curiosity, continuous learning, and operational excellence. Opportunity to contribute directly to the development and evaluation of advanced AI systems.