Rainmaker Fellow, Machine Learning

Rainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.

Rainmaker collects unusual atmospheric datasets because we build sensors, operate aircraft, fly into clouds, and deliberately intervene in atmospheric systems. Our long-term advantage depends on turning those observations into better estimates, forecasts, and operational decisions.

The Rainmaker Machine Learning Fellowship is a paid, full-time research appointment for exceptional undergraduate and graduate students, postdoctoral researchers, recent graduates, and other early-career researchers.

As a fellow, you will join Rainmaker's R&D team and work alongside our researchers on a scoped machine-learning project drawn from Rainmaker's current research priorities and defined in close collaboration with your research lead or mentor. Project matching will consider available data, mentor capacity, team needs, and your background. You will take responsibility for a concrete workstream while contributing to the broader team's research, reviews, and technical decisions.

You will work with real sensor and operational data, establish credible baselines, build and evaluate models, and leave behind a durable dataset, system, or research artifact that Rainmaker can continue using. Fellows are not expected to arrive with an independent research agenda or define a project in isolation.

Rainmaker is accepting expressions of interest while aggressively building its dedicated ML capability. Applications may be reviewed before a specific project and start date are finalized. A fellowship will begin only after the fellow is matched with a ready project, usable data, and a credible hands-on ML mentor.

Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our ML team may pursue include:

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.

$8,000 per month