Environmental Analyst AI Training Evaluator

You will help improve AI systems by creating expert training examples and reviewing AI-generated environmental and geospatial content. Your feedback will focus on accuracy, relevance, and alignment with real environmental and energy workflows.

The work is project-based, ongoing, remote, and asynchronous. You will use independent professional judgment while supporting AI research teams.

• Create training data based on environmental and energy-industry workflows.
• Review AI-generated content for accuracy, relevance, and sound environmental and geospatial practices.
• Give clear written feedback on environmental assessment, GIS analysis, and renewable energy siting.
• Apply professional judgment independently during active projects.

What it pays and takes

The work does require practical experience in environmental analysis, GIS, renewable energy siting, or a related energy-industry role.

• Pay: Up to $85 per hour.
• Location: Open to candidates in the United States; the work is remote.
• Schedule: Flexible and asynchronous, with no minimum hour requirement; typical participation is about 5 to 20 hours per week during active projects.
• Language: Fluent in English.
• Experience: At least two years of professional experience in environmental analysis, GIS, renewable energy siting, or an equivalent energy-industry role.
• Relevant roles include Environmental Analyst, GIS Analyst, Interconnection Specialist, Solar Design Engineer, or a comparable position.
• Tools: Hands-on experience with ArcGIS, Oracle MDM, or Aurora Solar.
• Skills: Strong written communication, careful attention to detail, and the ability to work independently.
• Helpful background includes environmental assessment, geospatial data practices, renewable energy workflows, and solar or interconnection analysis.

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. Contributors create examples and review model responses, while people with specialist knowledge help make AI outputs more accurate and useful in their field.