The Work
As a Staffing Supply Coordinator, you will support resource planning and contributor deployment across several AI training projects. The work includes workforce coordination, data management, project capacity tracking, and assigned chatbot comparison and assessment tasks.
The supplied role title describes AI training supply coordination, while the partner title is Staffing Supply Coordinator. Review the duties and qualifications before applying to make sure they match your experience.
- Allocate and move contributors based on project demand.
- Maintain deployment, status, performance, and capacity data.
- Monitor project capacity, throughput, onboarding, and offboarding.
- Coordinate access, training, and instructions for contributor movements.
- Track resource requests through completion and identify capacity risks or data issues.
- Complete clear shift handoffs and communicate pending actions.
- Suggest improvements to resource planning processes and systems.
- Compare multiple chatbot responses and provide final assessments.
What It Pays and Takes
This is a remote, project-based freelance engagement lasting 16 weeks. It is listed as an entry-level opportunity, while operations, workforce management, or resource planning experience is required.
- Pay: No rate is provided.
- Schedule: 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.
- Time commitment: 20 or more hours per week.
- Location: Remote and open worldwide.
- Language: Professional fluency in English.
- Required experience: Operations coordination, workforce management, or resource planning.
- Required skills: Spreadsheets, data management, organization, stakeholder management, clear communication, and accurate decisions under pressure.
- Helpful background: AI training or data annotation experience.
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 systems that learn from examples, including reviewing chatbot responses and rating model outputs. People with strong operations or subject skills help keep this work accurate, organized, and useful for improving AI systems.