Rainmaker Fellow, Atmospheric Science

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 needs quantitative methods that translate atmospheric conditions into operational and program-design decisions. Fellows work on analyses intended to change what Rainmaker builds, where it operates, or how it evaluates results.

About the Fellowship

The Rainmaker Atmospheric 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 atmospheric-science team and work alongside our researchers on a scoped quantitative project drawn from Rainmaker's current scientific and operational 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 analytical workstream while contributing to the broader team's research, reviews, and operational decisions.

Fellows are not expected to arrive with an independent research agenda or define a project in isolation. You will be expected to turn an important atmospheric or operational question into a rigorous analysis and a result the team can continue using.

Examples of the Work

Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our quantitative atmospheric-science team may pursue include:

Estimating seedable hours and program opportunity for a proposed or existing operating region.

Quantitatively evaluating cloud-seeding operations, targeting decisions, or factors associated with precipitation yield.

Building ground-truth datasets and validation methods for forecasting, retrieval, or machine-learning systems.

Verifying forecasts or automating a meteorological analysis used by operations.

Comparing program designs, targeting strategies, or operational constraints across candidate regions.

What You'll Do

Work with your research lead to translate an atmospheric, operational, or program-design question into measurable quantitative criteria.

Build and document the quality-controlled dataset required for the assigned project.

Apply appropriate statistical, climatological, geospatial, verification, or decision-analysis methods.

Compare results across relevant reanalysis, NWP, satellite, radar, terrain, operational, or in-situ data sources.

Incorporate practical operational constraints when they are relevant to the question.

Quantify uncertainty, missingness, bias, and sensitivity to assumptions.

Create maps, summaries, and decision-oriented outputs for scientists, operators, and program designers.

Build a documented and reusable workflow that the team can extend to future questions.

Deliver a final artifact such as a dataset, analysis, evaluation framework, operational tool, technical report, or presentation.

What We're Looking For

Current undergraduate, master's, or PhD students; postdoctoral researchers; recent graduates; and other early-career researchers are all eligible.

Background in meteorology, atmospheric science, applied mathematics, statistics, physics, hydrology, geospatial science, or a related field.

Strong Python and quantitative-analysis ability.

Experience with meteorological, climate, geospatial, radar, satellite, or model data.

Ability to make ambiguous definitions explicit and test the sensitivity of results to assumptions.

Strong scientific judgment about uncertainty, representativeness, and the difference between an analytical proxy and operational ground truth.

Clear written and verbal communication.

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

Particularly Relevant Experience

GRIB, netCDF, xarray, reanalysis, NWP, radar, satellite, terrain, or operational datasets.

Climatology, forecast verification, spatial statistics, time-series analysis, uncertainty quantification, or decision analysis.

Cloud microphysics, precipitation, orographic meteorology, convection, weather modification, or field operations.

Reproducible scientific workflows and decision-oriented visualization.

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 atmospheric-science team can continue using. The work should include clear uncertainty and sensitivity analysis and should not present an analytical proxy as more certain than the underlying observations support.