AI Data Quality Lead & Team Coach

SpaceXAI; Palo Alto (CA); Remote; USD 104,000 - 170,000; Full time

Job summary

SpaceXAI is seeking a hands-on Leader of AI Tutors to steer a team of full-time staff and contractors delivering high-quality training data and evaluations for our models.

You will set data-quality standards, review domain work, and drive continuous improvement while actively participating in labeling tasks and collaborating with Engineering to translate model needs into actionable labeling strategies.

Qualifications

• Bachelor's degree or higher in engineering field.
• 2+ professional engineering experience or a Master’s degree in engineering or related field.
• Basic understanding of AI and machine learning concepts and data impact.

Responsibilities

• Own end-to-end quality and delivery for assigned Human Data projects, reviewing work for accuracy and adherence to guidelines.
• Lead, coach, and performance-manage a team of AI Tutors, including reviews and action plans.
• Oversee labeling and reviewing tasks to maintain high standards and model best practices.
• Ensure taxonomy management and quality assurance processes.
• Drive process improvements and track performance via KPI dashboards.
• Develop training materials, practice tasks, and certification benchmarks.
• Collaborate with Human Data Managers, Team Leads, and Engineering to translate model needs into labeling strategies.
• Develop talent, maintain accountability, and support disciplinary actions as needed.
• Document outcomes, propose iterations, and report status, risks, and results.
• Voice of tutors to foster collaboration across Human Data organization.

Skills

Data quality metrics

Annotation processes

Team leadership

AI training

SQL

Education

Bachelor's degree in Engineering

Master's degree in Engineering

Tools

Data labeling tools

Quality assurance tools

Job description

SpaceXAI is seeking a hands-on Leader of AI Tutors to steer a team of full-time staff and contractors delivering high-quality training data and evaluations for our models.

You will set data-quality standards, review domain work, and drive continuous improvement while actively participating in labeling tasks and collaborating with Engineering to translate model needs into actionable labeling strategies.