Earth Science and Geology Expert for AI Evaluation

You will create and review professional data used to test AI models on geoscience and climate questions. Your work will show whether model responses are physically accurate, appropriately uncertain, and based on sound earth science.

You will use observations and geological interpretation to build realistic scenarios, explain geological reasoning, and judge model answers using evidence from outcrops, cores, and models.

• Write realistic geoscience scenarios from field observation through interpretation.
• Create reference answers that explain geological processes, reasoning, and timescales.
• Rate AI responses to geoscience and climate questions for physical accuracy.
• Identify answers that sound fluent but contain scientifically incorrect earth science.
• Apply process-based and evidence-based judgment to model outputs.

This is remote, asynchronous independent contractor work on an ongoing project. You set your schedule within the project commitment and work in English.

• Pay: $60 to $110 per hour, paid weekly via Stripe.
• Time: At least 20 hours per week, with no stated maximum.
• Location: Open to candidates in the eligible countries listed for this role.
• Education: An advanced degree in geology, earth science, or a closely related field, or equivalent professional experience.
• Experience: At least two years of research, industry, or field experience in the geosciences.
• Specialty: Depth in sedimentology, hydrology, geophysics, volcanology, climate science, or a related area.
• Communication: Strong written English and the ability to explain scientific reasoning clearly.
• Helpful background: A PhD, professional geologist licensure, GIS, remote sensing, geospatial modeling, or experience in energy, mining, environmental consulting, or government survey work.

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is where people find AI training and data-labeling work and start their application.

AI training is the human work behind modern artificial intelligence. Contributors write examples, review model responses, and provide expert judgments so AI systems can produce more accurate and useful results.

Specialist knowledge matters because experienced professionals can assess whether an answer is scientifically sound, explain why, and identify errors that general review may miss.