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Geology Quality Assurance Lead (QAL)

SME Careers • Remote

Education

PhD

Type

Hourly

Pay Rate

$70/hr

Listed

53d ago

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What We Know About This Role

Interview
Required i As stated in the listing: “Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.”

About this role

From the SME Careers listing

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.

Key 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.

Your profile

  • 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.

🌍 Geographic Availability This role is available to qualified experts globally. However, as a U.S.-based company, SME Careers complies with U.S. export control and sanctions regulations. Eligibility depends on your country of residence and any applicable trade restrictions. Some projects may have additional language or region-specific requirements.

💰 Pay Rate May Vary Based on Your Location Your actual compensation will be determined based on your location and local market conditions. See regional job listings for localized pay information, or the application process will clarify your specific rate.

If you're unsure whether you're eligible, proceed with the application—SME's screening will determine your jurisdiction status and applicable compensation.

How long hiring takes

Across the AI training platforms we refer candidates to, the median gap between referral and hire is about 30 days. It varies by platform and role, so treat it as a rough guide for this one.

Within 2 weeks
~25%
Within 6 weeks
~60%
Within 3 months
~80%

Why this role

This Earth Sciences / Geology Quality Assurance Lead role pays $70 per hour to oversee AI-generated geology and earth science content. The review covers geologic reasoning, terminology quality, and spatial and temporal context, meaning a reviewer has to catch errors in things like plate-tectonics timelines or mineral classification that a non-specialist would miss entirely. Someone with a geoscience background who's comfortable with unit handling and data precision in scientific writing is what the role is built around.

Talent pool

We're light on STEM candidates

We've matched 65 people with a STEM background against 726 STEM listings we've tracked, so most go out without one. Set up a profile and we'll consider you for a role like this one.

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Common questions

How does SME Careers pay freelancers?

Weekly, through Deel. Deel also handles tax compliance, which is how SME Careers pays contractors across more than 40 countries without running its own international payroll.

Is SME Careers work ongoing or project-based?

Project-based. Assignment duration and availability vary by project, but consistently high-quality work earns priority access to whatever comes next.

Is academic-niche AI training a continuous job?

Usually not. Work runs in campaigns, like training a model specifically on quantum mechanics, that might last a few weeks. Treat it as a high-paying fellowship or grant rather than a permanent daily job.

Is academic-niche AI training just data labeling?

No, it's closer to academic research. Expect to write or verify complex proofs, solve advanced equations, or check the logic behind a model's step-by-step reasoning. The goal is teaching AI systems to reason deeply within your specific field.

What does STEM work look like for a Geology Quality Assurance Lead (QAL)?

Tasks here are scoped to STEM, not generic labeling. As a Geology Quality Assurance Lead (QAL), expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to STEM) rather than following a one-size-fits-all rubric. If you don't have hands-on STEM background, this is likely not the right listing to start with.

What specific skills does this listing call for?

LLM evaluation, Scientific review, Mineralogy, and Plate Tectonics are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.

How much does this specific role pay?

This listing is posted at $70/hr, a per-task rate. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.

What happens when I click Apply on this listing?

You'll be taken to SME Careers's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.

Is a PhD required?

For this specific role, yes, or near-equivalent professional depth. The credential gate is enforced at the assessment stage, not just on paper. That said, active PhD candidates and people with equivalent published research have qualified without a formal degree. The assessment is the real filter.

Is there an interview?

Yes. Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Who is behind SME Careers?

SME Careers is the expert talent division of SuperAnnotate, an AI data infrastructure platform, connecting domain experts with high-level AI training projects.