Climate Modeling Machine Learning Expert

Job Description

• This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to climate modeling and earth system science. You will complete tasks at the intersection of machine learning and climate systems — including model development, climate projection, parameterization schemes, and research tasks applied to long-term climate dynamics, carbon cycle modeling, and climate impact assessment. 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 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 climate modeling and earth system problems
• Professional Growth – Sharpen your skills on varied, challenging climate datasets and earth system models

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

Company Description

• AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.