Spatial Data Analyst (GIS AI Evaluation)

This role involves evaluating how well AI systems understand geographic information systems, spatial data analysis, and environmental mapping. You will create domain-specific prompts, review generated responses, and give clear feedback on accuracy, relevance, and technical quality.

The listing is titled Spatial Data Analyst, while the listed duties focus on GIS AI response evaluation. Review these duties and qualifications before applying.

• Create prompts based on GIS, spatial analysis, and environmental mapping workflows.
• Review AI-generated responses for accuracy, relevance, and understanding of spatial data concepts.
• Explain strengths, errors, and ways AI-generated content could improve.
• Use QGIS and related technical tools when assessing domain-specific work.
• Work independently and communicate technical feedback asynchronously.

What it pays and takes

This is remote, asynchronous, ongoing, project-based contract work. Work is available according to project needs, and the structured schedule is 20+ hours per week.

• Pay: Up to $125 per hour.
• Location: Open to candidates in the United States.
• Language: Professional fluency in English.
• Schedule: Flexible, with 20+ hours per week.
• Experience: At least one year using QGIS or related technical tools.
• Skills: Hands-on experience with GIS, spatial data analysis, or environmental mapping.
• Skills: Ability to write domain-specific prompts and evaluate AI-generated responses.
• Skills: Strong judgment when assessing GIS tasks and spatial data workflows.
• Helpful background: Real-world QGIS experience in spatial analysis or environmental mapping, plus experience giving technical feedback asynchronously.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to join and helps people find AI training and data-labeling work in one place.

About AI training work

AI training is the human work behind systems that generate and understand content. Contributors prepare examples, review model responses, and give feedback so AI systems perform better, while people with specialized experience can contribute informed evaluations in their field.