AI Trainer (Earth & Environmental Sciences)

About the Role

Pay: $60–$120/hour equivalent, paid per accepted task Time Commitment: 15+ hours per week Schedule: Flexible micro1 is looking for Computational Earth and Environmental Scientists with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems. You will use your scientific expertise and programming skills to create challenging technical tasks, evaluate model-generated code and analyses, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have completed substantive AI-training work involving earth, climate, environmental, or geospatial content. Public Proof of Skill Strong applicants should provide evidence such as • Peer-reviewed publications or preprints involving computational methods • Public geospatial, climate, ocean, or environmental analysis code • Google Scholar, ORCID, or equivalent research profiles • Open-source scientific software contributions • Public datasets, models, notebooks, maps, or simulations • Publicly documented forecasting, remote-sensing, or environmental projects Application Process • Submit your application, AI-training experience, specialization, and public technical evidence. • Complete an AI interview containing technical and integrity checks. • Complete a hiring-manager review. • If selected, complete project onboarding and begin your first assigned task. Compensation • Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.

Responsibilities & Tasks

• Create realistic computational earth-science tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated code, geospatial analyses, simulations, forecasts, and technical deliverables.
• Identify issues involving coordinate systems, spatial or temporal data, model assumptions, uncertainty, numerical methods, or reproducibility.

Qualifications & Requirements

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency with Python.
• Deep expertise in at least one relevant earth or environmental-science specialization.
• Hands-on experience using computational methods for geospatial analysis, modeling, simulation, forecasting, remote sensing, statistics, or reproducible research.
• Relevant specializations may include geoscience, geology, geophysics, atmosphere and climate, meteorology, ocean and marine science, hydrology, remote sensing, environmental science, and sustainability.
• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers’ technical submissions.
• Experience with NumPy, SciPy, pandas, xarray, GeoPandas, GDAL, rasterio, NetCDF, or comparable tools.
• Experience validating computational results against observations, measurements, or established models.
• Availability to begin immediately.

Eligibility