Radar Meteorologist

Rainmaker Technology Corporation•El Segundo, CA

2d•$120,000 - $160,000•Onsite

About The Position

Rainmaker is hiring a Radar Meteorologist for its Atmospheric Intelligence team in El Segundo. The role turns weather radar observations into a clearer understanding of clouds, precipitation, and the effects of cloud-seeding operations. The meteorologist will develop and apply methods for radar quality control, precipitation estimation, and analysis of seeded and natural precipitation, spanning storm case studies, repeatable analysis workflows, and radar-observation design for field campaigns. The role works closely with radar scientists, meteorologists, software engineers, and field teams to make these methods useful across Rainmaker’s programs. The position supports cloud-seeding science by improving observations, analysis, and validation, and by helping translate atmospheric science into better targeting decisions and credible estimates of additional precipitation. The successful candidate will produce reproducible analyses and communicate findings that inform operational decisions and future experiments. The role is based onsite in El Segundo and may involve travel to field deployment sites as needed.

Requirements

A degree in meteorology, atmospheric science, physics, engineering, or a related field, or equivalent practical experience.
Demonstrated experience using weather radar observations to answer meteorological questions in research or operations.
Strong understanding of radar measurement principles, Doppler and polarimetric variables, sampling geometry, and common sources of error.
Knowledge of cloud microphysics and precipitation processes, including interpretation of observations in mixed-phase and winter weather.
Proficiency in Python for scientific data processing, analysis, and visualization.
Experience evaluating scientific methods against independent observations, diagnosing bias, and quantifying uncertainty.
Ability to take an open scientific question through data analysis to a defensible conclusion, using reproducible code and clear documentation.
Clear communication, constructive scientific collaboration, and willingness to work onsite in El Segundo and travel for field campaigns as needed.

Nice To Haves

An MS or PhD with research focused on radar meteorology, precipitation estimation, or cloud microphysics.
Experience with cloud-seeding research, orographic precipitation, snowfall retrievals, or attribution of small signals within natural variability.
Hands-on experience deploying, calibrating, or operating research radars and designing scan strategies.
Experience with tools such as Py-ART or xradar and data from research radars, NEXRAD, or MRMS.
Experience integrating radar with disdrometers, precipitation gauges, aircraft or UAS measurements, or numerical weather prediction.

Responsibilities

Analyze scanning and vertically pointing radar observations to characterize cloud structure, precipitation processes, and potential seeding signatures.
Develop and improve radar quality control, addressing clutter, interference, attenuation, calibration bias, and terrain blockage.
Develop and evaluate quantitative precipitation estimates for rain and snow, accounting for sampling limitations and retrieval uncertainty.
Combine radar data with seeding release records, winds, soundings, satellite observations, and in situ measurements to assess individual operations.
Design analyses to distinguish potential seeding responses from natural variability, test alternative explanations, and establish what observations support.
Help design radar siting and scan strategies for capturing cloud volumes and precipitation relevant to operational targeting and validation.
Build reproducible Python workflows for processing, visualization, and analysis, and partner with software engineers to automate methods across programs.
Support field campaigns and radar troubleshooting with field teams, including travel to deployment sites as needed.
Communicate findings through figures, technical reports, and reviews to inform operational decisions and future experiments.
Within the first 90 days, learn the radar systems, data workflows, and validation methods by working through a representative seeding event; deliver a reproducible case study; implement and evaluate a prioritized improvement against an agreed baseline; and propose further analysis and observing improvements with measures of success.