Remote Medicine AI Team Lead - Data Quality & Coaching

Jobgether SRL; United States; Remote; USD 104,000 - 170,000; Full time

Benefits offered by this job

Remote work options

Equity opportunities

401(k) plan

Job summary

Jobgether SRL is hiring for a leadership role overseeing Human Data projects in a data-intensive AI environment. You will guide a distributed team of AI Tutors, ensure high-quality data labeling, and continuously optimize processes to meet strict clinical-data standards.

The role emphasizes coaching, performance management, and collaboration with data managers and engineering to translate model requirements into actionable labeling guidelines.

Qualifications

• Experience in data labeling, AI training or evaluation in a clinical/medical domain.
• Understanding of AI/ML concepts and their impact on data quality.
• Bachelor’s or higher in medicine or related discipline; or 5+ years of relevant work.
• Clinical data workflows familiarity (labs, imaging, EHR, etc.).
• Past people management or team-lead experience preferred.

Responsibilities

• Own end-to-end quality and delivery for assigned Human Data projects.
• Lead and coach a team of AI Tutors with performance reviews.
• Participate in labeling and review activities to maintain high standards.
• Maintain QA processes, taxonomy management, and guidelines.
• Identify bottlenecks and implement process improvements.
• Track performance via KPI dashboards for quality and throughput.

Skills

Data labeling

AI training

Performance coaching

Analytical KPIs

SQL knowledge

Education

Bachelor's degree in medicine or related field

MD/DO/PhD or equivalent

5+ years of relevant experience (in lieu of degree)

Tools

Notion

Axiom

JIRA

Linear

Job description

Jobgether SRL is hiring for a leadership role overseeing Human Data projects in a data-intensive AI environment. You will guide a distributed team of AI Tutors, ensure high-quality data labeling, and continuously optimize processes to meet strict clinical-data standards.

The role emphasizes coaching, performance management, and collaboration with data managers and engineering to translate model requirements into actionable labeling guidelines.