AI Trainer (Earth & Environmental Sciences)

micro1; United States; Remote; USD 70,000 - 120,000; Full time

Job summary

micro1 seeks a skilled researcher to apply AI methods to earth and environmental science problems. The role focuses on training, evaluating, and benchmarking AI systems for scientific tasks, leveraging Python and geospatial data pipelines.

Candidates should have deep domain expertise in at least one earth science area, and be adept with computational methods for modeling, forecasting, remote sensing, or reproducible research. Immediate availability is preferred.

Qualifications

• Experience training AI systems for scientific tasks.
• Proficiency in Python programming.
• Deep earth/environmental science expertise.
• Experience with geospatial analyses and computational methods.
• Availability to begin immediately.

Skills

AI training

Python

Geoscience

Geospatial analysis

Computational methods

Statistics

Remote sensing

Tools

NumPy

SciPy

pandas

xarray

GeoPandas

GDAL

rasterio

NetCDF

Job description

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.

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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