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.