Atmospheric Modeling Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to atmospheric modeling and climate science. You will complete tasks at the intersection of machine learning and atmospheric systems — including model development, data assimilation, and research tasks applied to weather prediction, climate projection, extreme event detection, and atmospheric dynamics. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge climate and weather ML research, and a strong addition to your research portfolio. Why Apply Flexible Time Commitment – Work on your schedule while tackling meaningful scientific challenges Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart Exceptional Pay – Project-based pay ranges from $150–$200/hour Portfolio Building – Gain experience applying ML to frontier atmospheric and climate modeling problems Professional Growth – Sharpen your skills on varied, challenging geophysical datasets and models Responsibilities Apply machine learning techniques to atmospheric modeling tasks including weather forecasting, climate downscaling, extreme event detection, and atmospheric state estimation Build and evaluate ML models trained on reanalysis, observational, and simulation data from atmospheric and climate systems Develop hybrid modeling approaches that integrate physical constraints and numerical weather prediction outputs with data-driven methods Conduct rigorous benchmarking of ML models against operational weather and climate baselines Document methodologies, experimental results, and technical approaches clearly and reproducibly Required Qualifications Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Journal of Climate, Geophysical Research Letters, or equivalent) Master's or PhD in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and atmospheric or climate modeling Strong problem-solving skills and ability to work independently on technical and research tasks Preferred Qualifications Hands-on experience with atmospheric and climate datasets and tools (e.g., ERA5, CMIP6, WRF, CESM, xarray, or similar) Familiarity with ML-for-weather frameworks and models (e.g., Pangu-Weather, GraphCast, FourCastNet, ClimaX, or similar) Experience with physics-informed neural networks or hybrid dynamical-statistical modeling approaches Background in TA'ing or teaching atmospheric science, climate modeling, or machine learning courses.