About the role
Pay: $60–$120/hour equivalent, paid per accepted task
Location: Global, fully remote
Job Type: Contractor
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.
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.
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.