Geology Quality Assurance Lead

Role Overview

This remote contractor position is for an hourly Geology Quality Assurance Lead who will oversee quality control, consistency, and trainer performance across geology and earth science AI training projects. The work centers on reviewing AI-generated content and reviewer output, checking them against project rules, giving detailed written feedback, and making sure all contributors meet the required quality bar.

You will evaluate scientific correctness, geological reasoning, terminology, spatial and temporal context, unit usage, data interpretation, clarity, formatting, instruction adherence, and compliance with project rubrics. The role also involves identifying repeated quality issues, sharing updates with trainers and QAs, supporting onboarding, maintaining documentation, and re-engaging contributors who are not active.

This is part of a fast-scaling AI data services company that produces training data for major AI companies and foundation model labs. Your work will help improve AI systems by ensuring geology and earth science training data is accurate, well explained, contextualized, documented, and aligned with client needs.

There is no live project available immediately for this role. If you are a fit, you will be contacted first when relevant opportunities become available, and you may also gain access to future projects through the expert network.

Selection Process

The hiring process includes an AI interview, a domain-specific assessment, and a conversation with a recruiter.

Responsibilities

• Review geology and earth science tasks regularly, identify quality concerns, send feedback through direct messages, and escalate repeated or serious problems.
• Check AI-written geology explanations, summaries, process descriptions, map or data interpretations, climate or hazard explanations, and step-by-step reasoning for accuracy and clarity.
• Keep trainers and QAs informed on Discord about guideline updates, project changes, workflow adjustments, quality expectations, and review standards specific to earth science.
• Answer trainer and QA queries clearly and quickly, especially on geologic timescales, rock and mineral identification, Earth systems, natural hazards, spatial reasoning, environmental interpretation, and rubric usage.
• Re-contact inactive contributors, encourage activation, track follow-ups, and report availability concerns when necessary.
• Create and update project documentation such as style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Plan and lead onboarding or training sessions to explain project requirements, workflows, rubrics, quality expectations, and geology-specific review rules.
• Make sure trainers and QAs apply review guidelines consistently and stay aligned as project requirements evolve.
• Flag content that is misleading, too confident, geologically implausible, environmentally unsupported, or lacking proper context.
• Improve workflows by finding recurring quality gaps and helping design scalable QA processes for geology and earth science AI training work.

Requirements

• A bachelor’s, master’s, or PhD in Geology, Earth Sciences, Geoscience, Environmental Science, Geophysics, Geochemistry, Hydrology, Paleontology, Oceanography, or a closely related discipline.
• Strong English communication skills for understanding guidelines, coordinating with teams, and writing clear feedback.
• At least 3 years of experience in geology or earth science research, teaching, fieldwork, environmental consulting, geospatial analysis, academic review, science communication, or similar work.
• Solid knowledge of plate tectonics, the rock cycle, mineralogy, stratigraphy, geologic time, structural geology, geomorphology, natural hazards, climate systems, hydrology, and Earth system processes.
• Ability to assess geology and earth science content against detailed rubrics and detect wrong processes, incorrect timescales, misleading causal claims, flawed map or data interpretation, unsupported environmental claims, and oversimplified explanations.
• Preferred familiarity with GIS, remote sensing, geologic mapping, field methods, core or log interpretation, geochemical data, climate datasets, Python or R, or scientific visualization.
• Prior experience leading or supporting remote teams of researchers, educators, reviewers, environmental specialists, annotators, or QAs is strongly preferred.
• Comfort using remote collaboration tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management platforms.
• Strong organizational skills and attention to detail, with the ability to maintain style guides, FAQs, trackers, onboarding resources, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong advantage.

Additional Information

This is an hourly remote contractor role. The position is focused on geology and earth science quality assurance. Availability for future projects through the expert network is part of the opportunity.

Interview Process

Applicants should expect an AI interview, a domain-specific task, and a recruiter interview.

Location

Remote