Senior Quality Lead (AI Annotation)

As a Senior Quality Lead, you will guide quality decisions across AI annotation and chatbot evaluation projects. You will turn detailed guidelines into clear instructions, improve reviewer consistency, and help strengthen how AI training data is produced and assessed.

The listing title identifies this role as a Senior Quality Lead, while the duties focus on AI annotation quality. Review the duties and qualifications before applying.

• Validate project setup and test new task templates.
• Mentor QA Leads, annotation teams, and reviewers.
• Lead calibration sessions and resolve ambiguous cases.
• Audit work against detailed guidelines and document quality decisions.
• Analyze audit results and recommend improvements to guidelines and training.
• Identify high-performing reviewers for accelerated workflows.
• Compare multiple chatbots and provide final assessments for defined evaluation tasks.

What It Pays and Takes

The role is a remote, project-based freelance engagement. It is structured as a contractor and part-time opportunity.

• Pay: Not specified in the provided role details.
• Schedule: 20+ hours per week.
• Project length: 16 weeks.
• Working pattern: Five working days per week, including weekends as required, with two weekdays off.
• Working hours: 7:00 a.m. to 4:00 p.m. Pacific Time.
• Location: Open worldwide and fully remote.
• Language: Fluent in English.
• Experience: 4 to 6 years of overall professional experience, including at least 2 years leading QA reviewers.
• Skills: Strong judgment in complex or ambiguous cases, careful guideline-based auditing, clear communication, and strong documentation.
• Helpful background: Annotation, data labeling, AI training data, multimodal projects, quality management, QA, team management, calibration, and reviewer mentoring.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind modern artificial intelligence. Contributors label data, review model responses, evaluate chatbots, and provide feedback so AI systems can learn from reliable examples.