About this role
In this hourly, remote contractor role, you will work as an Earth Sciences / Geology Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across geology and earth science AI training projects. You will review AI-generated earth science/geology content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for scientific accuracy, geologic reasoning, terminology quality, spatial and temporal context, unit handling, data interpretation, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong earth science/geology expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your earth science/geology quality leadership will directly help improve the world’s premier AI models by ensuring that geology and earth science training data is accurate, contextualized, clearly explained, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
Requirements
- 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.
- 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 is preferred.
- Experience leading or supporting remote teams of researchers, educators, reviewers, environmental specialists, annotators, or QAs is strongly preferred.
- 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.
- Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.
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.
Skills
- Climate Systems
- LLM Evaluation
- Earth Sciences
- Geology
- Mineralogy
- Plate Tectonics
- AI Training
- Geoscience
- Trainer Feedback
- Scientific Review
Additional skills
- Quality Assurance
- Geology
- Earth Sciences
- Environmental Science
- Geoscience
- Geospatial Analysis
- GIS
- Remote Sensing
- Geologic Mapping
- Field Methods
- Stratigraphy
- Structural Geology
- Mineralogy
- Plate Tectonics
- Geomorphology
- Hydrology
- Geochemistry
- Core Logging
- Well Log Interpretation
- Natural Hazards
- Climate Science
- Earth System Science
- Data Interpretation
- Scientific Writing
- Technical Editing
- Rubric-Based Review
- Academic Peer Review
- Data Annotation
- LLM Evaluation
- Python
