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