Atmospheric Modeling Machine Learning Expert

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

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

Employment type: Contract Time commitment: 10 hours/week Location: Remote