Climate Modeling Machine Learning Expert

Responsibilities - Apply machine learning techniques to climate modeling tasks including climate projection, parameterization emulation, bias correction, and climate impact modeling - Build and evaluate ML models trained on earth system model outputs, observational records, and paleoclimate data - Develop learned parameterization schemes for subgrid-scale processes such as convection, clouds, and ocean mixing - Conduct rigorous evaluation of ML climate models against observational benchmarks and physics-based 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, Nature Climate Change, Journal of Climate, Geophysical Research Letters, or equivalent) - Master's or PhD in Climate Science, Earth System Science, Atmospheric Science, Applied Mathematics, Computer Science, or a related quantitative field - Demonstrated expertise in both machine learning and climate modeling or earth system science - Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred qualifications - Hands-on experience with earth system models and climate datasets (e.g., CESM, GFDL, CMIP6, ERA5, or similar) - Familiarity with ML-for-climate research (e.g., NeuralGCM, ClimaX, climate emulators, or learned parameterizations) - Experience with physics-informed neural networks, uncertainty quantification, or causal inference applied to climate systems - Background in TA'ing or teaching climate science, earth system 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 climate modeling and earth system problems - Professional Growth – Sharpen your skills on varied, challenging climate datasets and earth system models

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