Rainmaker Fellow, Machine Learning

This is a paid, full-time research fellowship in machine learning at Rainmaker, a company pioneering cloud-seeding technology. As a fellow, you will join the R&D team and work on a scoped ML project, translating scientific questions into measurable problems, building datasets, establishing baselines, and training/evaluating models. You will collaborate with atmospheric scientists and deliver a durable artifact like a benchmark dataset or model. The fellowship is on-site in El Segundo and lasts three to six months.

Responsibilities

• 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.

Requirements

• 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.

Nice to have

• 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.

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