The work
You will lead quality operations for AI annotation projects that compare chatbot responses and produce final assessments. The work focuses on consistent reviews, clear quality decisions, and delivery within required turnaround times.
- Lead Senior QA Leads and QA Leads across shifts.
- Set review priorities, sampling plans, and quality coverage.
- Create and use audits, scoring rubrics, and calibration processes.
- Track accuracy, agreement, error rates, review throughput, and turnaround time.
- Identify root causes and drive corrective actions with team leads.
- Resolve guideline questions and document quality decisions.
- Share quality insights after each delivery and report performance to the Quality Manager.
- Support quality reviews and quality-based incentive programs.
What it pays and takes
This is a remote, project-based freelance role lasting 16 weeks. The role is listed as entry level and requires strong quality operations skills, careful review work, and the ability to compare multiple chatbot responses consistently.
- Pay: Not provided in the listing.
- Schedule: 20 or more hours per week, 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: Remote and open to candidates worldwide.
- Language: Fluent in English.
- Experience: Five to seven years of overall professional experience, including two to three years leading QA or quality operations teams.
- Required skills: Sampling, auditing, calibration, root-cause analysis, strong attention to detail, systemic issue identification, communication, and stakeholder management.
- Helpful background: Annotation, data labeling, AI training data projects, multimodal AI annotation workflows, and quality measurement.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training work is the human side of building artificial intelligence. People review examples, compare model responses, and label or assess data so AI systems can produce more useful and reliable results.