M
AI Trainer (Earth & Environmental Sciences)
micro1
Contract type
Freelance
Work mode
Remote · Worldwide
Experience
3 years
Job description
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. The role involves creating challenging technical tasks, evaluating model-generated code and analyses, and establishing objective standards for correct solutions.
At a glance
- Employment
- Contract · Independent contractor
- Work mode
- Remote · Worldwide
- Pay
- USD 60 – 120 / hour
What you'll do
- 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.
What we're looking for
- 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.
- Strong applicants should provide public proof of skill 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, or publicly documented forecasting,
Nice to have
- 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.
How to get hired
- 1Submit your application, AI-training experience, specialization, and public technical evidence.
- 2Complete an AI interview containing technical and integrity checks.
- 3Complete a hiring-manager review.
- 4If selected, complete project onboarding and begin your first assigned task.