AI Trainer – Earth & Environmental Sciences - Remote

Location: Global
Work Arrangement: Fully Remote
Employment Type: Independent Contractor
Time Commitment: 15+ hours per week
Schedule: Flexible
Compensation: $115,200–$230,400/year equivalent
Hourly Equivalent: $60–$120/hour, paid per accepted task

Annualized compensation is an illustrative equivalent based on 40 hours/week × 48 weeks/year. This is an output-based engagement, not a guaranteed salary. Payment is determined by accepted tasks that meet project specifications and quality standards. Actual earnings depend on task availability, completion time, and approved work.

About the Role

We are seeking experienced Computational Earth and Environmental Scientists with prior professional AI-training or model-evaluation experience to contribute to the development of advanced AI systems.

In this fully remote contractor role, you will combine scientific expertise with programming skills to create challenging computational tasks, evaluate AI-generated code and analyses, identify technical and scientific errors, and establish objective standards for accurate solutions.

Your work will focus on earth, climate, environmental, and geospatial applications. You will help assess how AI models interpret complex scientific data, perform computational analyses, and solve real-world environmental and geoscience problems.

Key Responsibilities

• Create realistic computational Earth and environmental science tasks that challenge advanced AI models.

• Develop clear task instructions, reproducible computational environments, reference solutions, and evaluation criteria.

• Evaluate AI-generated code, geospatial analyses, simulations, forecasts, and other technical deliverables.

• Identify errors involving coordinate reference systems, spatial and temporal data, model assumptions, uncertainty, numerical methods, and reproducibility.

• Assess the scientific validity, computational accuracy, and reliability of model-generated results.

• Apply domain expertise to establish objective standards for evaluating technical solutions.

• Provide rigorous feedback to support improvements in AI model performance across earth and environmental science applications.

Required Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.

• Strong practical proficiency in Python.

• Deep expertise in at least one relevant Earth or environmental science specialization.

• Hands-on experience applying computational methods to geospatial analysis, modeling, simulation, forecasting, remote sensing, statistics, or reproducible scientific research.

• Strong analytical reasoning and technical problem-solving abilities.

• Ability to evaluate complex scientific outputs and identify methodological, computational, and data-related issues.

Relevant specializations include:

• Geoscience and geology

• Geophysics

• Atmospheric science and climate science

• Meteorology

• Oceanography and marine science

• Hydrology

• Remote sensing

• Environmental science

• Sustainability

Public Proof of Technical Expertise

Applicants should be prepared to provide verifiable evidence of their scientific and computational work, such as:

• Peer-reviewed publications or preprints involving computational methods.

• Public geospatial, climate, ocean, or environmental analysis code.

• Research profiles such as Google Scholar or ORCID.

• Contributions to open-source scientific software.

• Public datasets, computational models, notebooks, maps, or simulations.

• Documented forecasting, remote-sensing, or environmental research projects.

Preferred Qualifications

• Experience creating AI-training tasks, reference answers, evaluation rubrics, automated graders, or benchmark problems.

• Experience reviewing and assessing technical submissions from other AI trainers.

• Familiarity with scientific and geospatial computing tools such as NumPy, SciPy, pandas, xarray, GeoPandas, GDAL, rasterio, and NetCDF, or comparable technologies.

• Experience validating computational results against observational data, field measurements, or established scientific models.

• Availability to begin immediately.

Application and Selection Process

Candidates should submit an application outlining their previous AI-training or model-evaluation experience, scientific specialization, and relevant public technical evidence.

The selection process includes:

1. Application Review: Assessment of AI-training experience, domain expertise, and public technical evidence.

2. AI Interview: Technical evaluation incorporating integrity checks.

3. Hiring Manager Review: Further assessment of qualifications and project suitability.

4. Project Onboarding: Selected candidates complete onboarding before beginning their first assigned task.

Compensation Structure

This is an output-based independent contractor engagement. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary, so earnings depend on accepted deliverables rather than guaranteed working hours.

Earth and environmental scientists with demonstrated AI-training experience, strong Python skills, and verifiable computational research expertise are encouraged to apply.