AI Trainer – Earth & Environmental Sciences - Remote

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 comp