WFH.teamOpen app$125k-$250k
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flexible
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What this posting tells you
Remote contractor role creating computational Earth and environmental science tasks and evaluating AI-generated technical work. Compensation is listed as $60–$120 per hour equivalent, paid per accepted task; task availability and completion time may vary. Requires prior paid AI-training or evaluation experience, strong Python skills, and relevant scientific expertise. Minimum commitment is 15 hours per week.
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Eligible countries are not stated.
Global
Not specified · Contractor
- Eligible hiring countries are not stated
Backend hiring on WFH.team
This role's listed pay is near the median among 200 comparable roles shown here. Category counts come from WFH.team's latest published remote job market snapshot.
Explore the remote job marketAI Trainer (Earth & Environmental Sciences) at micro1
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.
Working remotely at micro1
micro1 builds human-expertise data and evaluation infrastructure for AI models and agents. It recruits core-team employees and domain experts for AI training projects.
- Headquarters
- San Francisco Bay Area, California, United States
- Founded
- 2022
Expert opportunities are remote and can be flexible. International applicants are considered, subject to country exclusions and each project's requirements. Core-team work arrangements vary by opening; check the individual posting.
Apply through the official posting. Expert applicants complete an AI interview and skills certification, followed by project selection and onboarding. Certification does not guarantee a project or immediate work.