Drilling Superintendent AI Response Evaluator

You will help improve AI systems that work with drilling and energy topics. Your work will draw on real experience in drilling, completions, well planning, and energy data systems. Review the duties and qualifications before applying: this role uses drilling-superintendent experience for AI response evaluation.

You will work independently and asynchronously, creating expert training examples and judging whether AI-generated responses are accurate and relevant to real drilling and energy operations.

• Create expert-level training data from professional drilling, completions, and energy operations experience.
• Evaluate AI-generated responses for accuracy and relevance in drilling and energy settings.
• Give clear, detailed feedback to improve AI understanding of drilling practices and energy data systems.
• Apply practical knowledge of WellView, SAP, ComboCurve, or AVEVA PI.

What it pays and takes

This is an ongoing, project-based opportunity with flexible remote participation. The role is listed as part-time contract work and is open to candidates in the United States.

• Pay: Up to $85 per hour.
• Time: 20 or more hours per week.
• Location: Open to candidates in the United States.
• Language: Fluent in English.
• Experience: At least two years in drilling, completions, or related energy operations.
• Background: Drilling engineer, completions engineer, well cost analyst, drilling superintendent, or an equivalent energy-industry role.
• Tools: Hands-on experience with at least one of WellView, SAP, ComboCurve, and AVEVA PI.
• Skills: Strong written communication, attention to detail, independent work habits, and the ability to assess AI responses for accuracy and relevance.
• AI experience: Prior AI experience is not required.

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 generate and understand text. Contributors create examples, review model responses, and give feedback, while people with specialized professional knowledge help make those systems more accurate in their fields.