Energy Professional AI Training Evaluator

You will use practical energy industry knowledge to create training data, review AI-generated content, and explain whether responses are accurate, relevant, and aligned with real-world practice. The work is remote and can draw on energy engineering, utility, renewable, regulatory, market, and operational experience.

• Create expert training data using energy tools, workflows, and professional experience.
• Evaluate AI-generated responses for accuracy, relevance, and real-world energy practices.
• Use professional judgment on reservoir simulation, AFE preparation, meter data validation, and load forecasting.
• Review workflows involving emissions reporting, predictive maintenance, rate case filing, and renewable generation forecasting.
• Write clear feedback, work independently, and maintain close attention to detail.

This is ongoing, project-based contract work that can be completed alongside other commitments.

• Pay: Up to $80 per hour.
• Work arrangement: Part-time contract work with a flexible, asynchronous schedule.
• Time requirement: 20+ hours per week.
• Location: Remote work open to candidates in the United States.
• Language: Professional fluency in English.
• Experience: At least four years of professional energy industry experience.
• Relevant backgrounds include reservoir, drilling, production, plant performance, reliability, utility, regulatory, renewable generation, environmental, power market, electrical, and field service work.
• Skills: Hands-on familiarity with energy workflows, tools, or operating practices, plus strong written communication and careful judgment.
• Work style: Ability to work independently in a remote, asynchronous environment.

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is where you find the opportunity and begin the application; it is not the employer or payer for this role.

OpenTrain helps people find and build careers in AI training and data labeling. AI training work uses human expertise to create examples and review model responses, so professionals with real industry knowledge can help AI systems produce more accurate and useful results.