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.
