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Geology Quality Assurance Lead; QAL

Remote / Online - Candidates ideally in
San Francisco, San Francisco County, California, 94199, USA
Listing for: AI Trainer Jobs
Remote/Work from Home position
Listed on 2026-10-09
Job specializations:
  • Science
    Geology / Geoscience, AI Evaluation
Salary/Wage Range or Industry Benchmark: 70 USD Hourly USD 70.00 HOUR
Job Description & How to Apply Below
Position: Geology Quality Assurance Lead (QAL)

Pay: up to $70/hour

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 Super Annotate, 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.

Responsibilities
  • Quality monitoring:
    Spot-check geology/earth science items, identify quality issues, provide ongoing feedback through DMs, and escalated 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,…
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