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Job Summary:
YO IT Consulting is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. They are seeking a Geology Quality Assurance Lead to oversee quality and consistency across geology and earth science AI training projects, ensuring that all contributors follow expected quality standards and that the output is scientifically accurate and well-documented.
Responsibilities:
• Quality monitoring: Spot-check geology/earth science items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Scientific review: Evaluate AI-generated geology explanations, earth science summaries, geologic process descriptions, map/data interpretations, climate or hazard explanations, and step-by-step reasoning for accuracy and clarity.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and geology/earth-science-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around geologic timescales, rock/mineral identification, earth systems, natural hazards, spatial reasoning, environmental interpretation, and rubric interpretation.
• Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
• Documentation: Create and maintain geology/earth science project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and geology/earth-science-specific review requirements.
• Quality alignment: Ensure all trainers and QAs apply geology/earth science review guidelines consistently and understand updates as projects evolve.
• Risk review: Flag misleading, overconfident, geologically impossible, environmentally unsupported, or poorly contextualized earth science claims.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for earth science/geology AI training projects.
Qualifications:
Required:
• Bachelor’s, Master’s, or PhD degree in Geology, Earth Sciences, Geoscience, Environmental Science, Geophysics, Geochemistry, Hydrology, Paleontology, Oceanography, or a closely related field.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
• 3+ years of experience in geology/earth science research, teaching, fieldwork, environmental consulting, geospatial analysis, academic review, science communication, or related workflows.
• Strong understanding of plate tectonics, rock cycle, mineralogy, stratigraphy, geologic time, structural geology, geomorphology, natural hazards, climate systems, hydrology, and earth system processes.
• Ability to evaluate earth science/geology content against detailed rubrics and identify issues such as incorrect geologic processes, wrong timescales, misleading causal claims, flawed map/data interpretation, unsupported environmental claims, or oversimplified explanations.
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation.
Preferred:
• Familiarity with tools or methods such as GIS, remote sensing, geologic mapping, field methods, core/log interpretation, geochemical data, climate datasets, Python/R, or scientific visualization.
• Experience leading or supporting remote teams of researchers, educators, reviewers, environmental specialists, annotators, or QAs.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review.
Company:
Our Core mission is to develop, deploy, or integrate artificial intelligence (AI) — including machine learning (ML), data analytics, automation, natural language processing (NLP), computer vision, and related technologies — to solve real-world problems, improve decision-making, automate repetitive tasks, and deliver intelligent solutions across industries. Founded in 2018, the company is headquartered in Abu Dhabi, Abu Dhabi Emirate, AE, , with a team of 51-200 employees. The company is currently Growth Stage.