Rainmaker Fellow, Machine Learning

El Segundo, CA

About Rainmaker

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.

About the Fellowship

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.

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.