AI Data Quality Lead — Remote, Equity Eligible

Lever, Inc.; España; Presencial; EUR 90.000 - 140.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Equity participation

Medical coverage

Retirement benefits

Disability & life insurance

Remote-friendly opportunities

Descripción de la vacante

Lever, Inc. is partnering to fill an Expert Team Lead, SWE position based in Spain. The role focuses on hands-on leadership for building and maintaining high-quality human-generated data supporting advanced AI model development.

You will lead AI Tutors while reviewing, evaluating, and improving technical work, balancing people leadership with software engineering rigor. You will establish quality standards, monitor KPIs, and translate model requirements into effective labeling strategies.

Formación

• Hands-on experience with data labeling, annotation, AI training or evaluation workflows.
• Proven ability to lead teams and coach staff in a fast-paced environment.
• Strong understanding of data quality metrics and quality assurance practices.

Responsabilidades

• Own end-to-end quality and delivery for assigned human data projects, reviewing work for accuracy and consistency.
• Lead, coach, and assess a team of AI Tutors, including performance reviews and development plans.
• Participate in labeling, annotation, evaluation, and review activities to maintain high standards.

Conocimientos

Team leadership

Data labeling workflows

KPI driven improvement

SQL experience

Stakeholder communication

Project management

Descripción del empleo

Lever, Inc. is partnering to fill an Expert Team Lead, SWE position based in Spain. The role focuses on hands-on leadership for building and maintaining high-quality human-generated data supporting advanced AI model development.

You will lead AI Tutors while reviewing, evaluating, and improving technical work, balancing people leadership with software engineering rigor. You will establish quality standards, monitor KPIs, and translate model requirements into effective labeling strategies.