AI Trainer (Earth & Environmental Sciences)

Overview

micro1 is seeking Computational Earth and Environmental Scientists with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems. This is a global, fully remote contractor role with flexible scheduling and a time commitment of 15+ hours per week.

Compensation is output-based and paid per accepted task. Task availability and completion time may vary.

What You’ll Work On

• 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.

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 with 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.

Preferred Qualifications

• 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.

Application Process

1. Submit your application, AI-training experience, specialization, and public technical evidence.
2. Complete an AI interview with technical and integrity checks.
3. Complete a hiring-manager review.
4. If selected, complete project onboarding and begin your first assigned task.

Public Proof of Skill

• 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.