Rainmaker Fellow, Machine Learning

Examples of the Work
Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our ML team may pursue include: Developing a short-range supercooled liquid water opportunity forecast using public NWP and Rainmaker observations. Predicting hail-core growth, motion, splitting, and decay from radar sequences. Building a bounded multimodal atmospheric-state reconstruction pilot. Improving microwave-sounder retrievals using Rainmaker observations. Modeling another scientific or operational problem selected with Rainmaker's ML and atmospheric-science teams.

What You'll Do
Translate a scientific or operational question into a measurable ML problem. Build or improve the training and validation dataset needed for the project. Establish simple, reproducible baselines before introducing more complex models. Train, evaluate, and debug models using held-out weather events, regions, or operating conditions. Quantify calibration, uncertainty, generalization, failure modes, and sensitivity to missing or biased data. Work closely with atmospheric scientists to define useful targets, ground truth, physical constraints, and operational success criteria. Produce clear, reusable code and documentation. Present your results to Rainmaker's scientists, engineers, operators, and technical leadership. Deliver a final artifact such as a benchmark dataset, model, prototype product, evaluation report, or research paper.

What We're Looking For
Current undergraduate, master's, or PhD students; postdoctoral researchers; recent graduates; and other early-career researchers are all eligible. Strong Python programming ability and experience with a modern ML framework. Evidence that you can independently build, test, and debug technical work. Strong quantitative reasoning and an ability to design credible experiments. Interest in noisy, sparse, multimodal, spatial, temporal, or physical data. Ability to make progress on ambiguous research problems while incorporating mentor feedback. Clear written and verbal communication. Availability for full-time, on-site work in El Segundo for the agreed appointment.

Particularly Relevant Backgrounds
Machine learning, computer science, applied mathematics, statistics, physics, meteorology, remote sensing, robotics, autonomy, geospatial analysis, or scientific computing. Forecasting, sequence modeling, computer vision, state estimation, sensor fusion, probabilistic modeling, data assimilation, or uncertainty quantification. Weather knowledge is valuable but not required.

What Success Looks Like
By the end of the fellowship, you will have answered a clearly defined technical question and produced a rigorous, reusable result that advances the team's work. Depending on the project, that might be a benchmark dataset, evaluated model, prototype product, forecasting or retrieval improvement, or a well-supported analysis of performance and failure modes. Success does not require a positive scientific result. A well-supported finding that the available data cannot answer the question—and a concrete recommendation for what Rainmaker should measure next—can be highly valuable.

Fellowship Details
Paid, full-time, and on-site in El Segundo. Three-to-six-month appointment, with four months as the standard duration. Rolling applications and flexible start dates based on project and mentor readiness. Possible consideration for future full-time roles, without any promise or expectation of conversion.

Compensation and Benefits
$8,000 per month Benefits: Full health coverage (medical, dental, and vision insurance) Lunch provided when working in-office and a fully stocked kitchenette Free EV charging at the HQ