Rainmaker Fellow, Satellite Remote Sensing

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.

Satellite observations provide the coverage Rainmaker needs to understand clouds across large regions, while our radar, aircraft, UAS, and in-situ sensors provide unusually valuable observations for validation and improvement.

About the Fellowship

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

You will join Rainmaker's satellite remote-sensing group and work alongside our researchers on a scoped project drawn from the team'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 computational workstream while contributing to ongoing retrieval, validation, automation, and operational-support work across the team.

Examples of the Work

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

Benchmarking and improving microwave-sounder retrievals using Rainmaker in-cloud measurements, radar, geostationary imagery, and model fields.

Fusing intermittent polar-orbiting observations with frequent geostationary imagery to track cloud properties between overpasses.

Automating an existing manual satellite-analysis workflow used by Rainmaker scientists or operators.

Validating cloud-phase, cloud-top, precipitation, moisture, temperature, or related satellite products against Rainmaker observations.

Developing a bounded retrieval or satellite data product for research or operational use.

What You'll Do

Acquire, process, collocate, and quality-control microwave, polar-orbiting, and geostationary satellite observations.

Reproduce an existing retrieval or operational baseline before testing improvements.

Validate satellite products against radar, NWP, soundings, surface observations, and Rainmaker aircraft, UAS, or in-situ measurements.

Quantify detection skill, bias, uncertainty, spatial representativeness, latency, coverage, and failure modes by meteorological regime.

Implement physically motivated, statistical, or ML retrieval improvements when justified by the project and data.

Build documented, reproducible workflows that other Rainmaker scientists can run and extend.

Present findings to satellite scientists, radar scientists, meteorologists, operators, and technical leadership.

Deliver a final artifact such as a collocation dataset, retrieval benchmark, automated data product, error analysis, fusion prototype, 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 quantitative and programming ability, preferably in Python.

Experience with atmospheric remote sensing, satellite meteorology, physics, applied mathematics, electrical engineering, computer science, or a related field.

Interest in microwave sounders, polar-orbiting observations, geostationary imagery, retrieval methods, or scientific data products.

Ability to implement a scientific method, establish a baseline, and validate the result carefully.

Comfort working with large, imperfect, multidimensional observational datasets.

High agency and the ability to take responsibility for a bounded workstream while collaborating with experienced researchers.

Clear written and verbal communication.

Availability for full-time, on-site work in El Segundo for the agreed appointment.

Particularly Relevant Experience

By the end of the fellowship, you will have answered a clearly defined scientific or operational question and delivered a trusted computational result the satellite remote-sensing team can continue using. Depending on the project, that might be a quality-controlled validation dataset, retrieval benchmark or improvement, error analysis, fusion prototype, automated data product, or operational workflow.

Success does not require a positive result. A rigorous conclusion about what the available observations can and cannot support can be as valuable as an improved retrieval.