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.
Radar is central to how Rainmaker observes storms, targets operations, evaluates atmospheric evolution, and learns from field programs. Fellows work with scientists and operators who use the resulting analysis in real decisions.
About the Fellowship
The Rainmaker Radar Science 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 radar-science 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 workstream while contributing to ongoing analysis, scientific review, and operational support across the radar team.
Examples of the Work
Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our radar team may pursue include:
Creating a radar-aircraft collocation atlas to investigate which radar structures and dual-polarization features contain useful information about measured supercooled liquid water.
Extending and validating an MRMS/MESH hail-event catalog and storm-object tracker.
Developing a benchmark for hail-core growth, motion, splitting, and decay.
Building a carefully controlled radar evaluation framework for cloud-seeding or hail-suppression operations.
Automating an existing radar-analysis workflow used by Rainmaker scientists and operators.
What You'll Do
Build quality-controlled radar datasets aligned with aircraft, UAS, satellite, model, surface, or operational observations.
Implement and validate radar-processing, feature-extraction, retrieval, storm-tracking, or statistical-analysis methods.
Investigate how results vary with storm type, range, terrain, temperature regime, observing geometry, and data quality.
Develop honest baselines and quantify uncertainty, false detections, selection effects, and failure modes.
Create case visualizations and scientific analyses that domain experts can inspect.
Avoid overstating what radar observations can establish about seedability or intervention effects.
Produce clear, reusable code and documentation.
Present findings to Rainmaker's radar scientists, meteorologists, operators, and technical leadership.
Deliver a final artifact such as a validated dataset, case atlas, tracking system, retrieval candidate, operational product, analysis protocol, 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 radar meteorology, atmospheric science, signal processing, remote sensing, image analysis, geospatial data, or a closely related field.
Ability to formulate a scientific question, implement an analysis, and validate the result carefully.
Comfort working with large, imperfect 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
Weather radar, polarimetric radar, MRMS, NEXRAD, quantitative precipitation estimation, storm-object tracking, cloud radar, or radar retrievals.
Scientific Python, xarray, geospatial processing, visualization, statistical modeling, or ML for physical data.
Cloud microphysics, mixed-phase clouds, hail, severe convection, or weather modification.
Aircraft, UAS, field-campaign, or instrument-validation data.
What Success Looks Like
By the end of the fellowship, you will have answered a clearly defined scientific or operational question and delivered a result the radar team can continue using. Depending on the project, that might be a validated dataset, case atlas, retrieval, tracking system, analysis framework, or automated operational workflow.
Success means producing a scientifically defensible result with clear quality controls, uncertainty, and attribution boundaries—not forcing a conclusion that the data cannot support.
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 project-specific start dates.
Attached directly to Rainmaker's radar-science group with a named mentor.
Possible consideration for future full-time roles, without any promise or expectation of conversion.
Publication may be supported when it does not compromise Rainmaker intellectual property or operational know-how.
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