AI Trainer (Earth & Environmental Sciences)
micro1
Required Qualifications
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.
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
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
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.
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