Head of Atmospheric Science - Onsite in San Francisco, CA

Roadrunner Venture Studio•San Francisco, CA

1d•Hybrid

About The Position

Roadrunner Venture Studios is recruiting a Head of Atmospheric Science for a portfolio company developing rainfall enhancement technology. You will lead the atmospheric science function and connect scientific validation with operational deployment. The scope includes meteorology, atmospheric prediction, modeling, data assimilation, field-data review, and scientific evaluation of deployments. Atmospheric modeling is a major part of a broader mandate. You will set scientific direction, work hands-on, and build the team needed to support a growing deployment program. This role includes significant equity. This is an early-stage startup building something that has not been built before. Expect weeks of 50 to 60 hours, sometimes more, with frequent travel and regular weekend work. Priorities will change quickly. If this working environment does not excite you, this job is probably not for you.

Requirements

Bachelor's degree or higher in atmospheric science, meteorology, physics, computer science, data science, or a related field.
Broad experience in atmospheric science, meteorology, weather prediction, numerical weather prediction, or atmospheric research.
Extensive experience configuring, running, and analyzing WRF.
Experience implementing WRFDA, DART, or similar data assimilation systems for research or operational use.
Strong knowledge of boundary layer meteorology, cloud microphysics, convective initiation, precipitation processes, and mesoscale atmospheric dynamics.
Experience interpreting forecasts and observations from radar, satellite, soundings, surface stations, or other meteorological systems.
Prior experience with artificial intelligence and machine learning weather modeling, weather emulators, or related atmospheric applications.
Experience running numerical weather prediction models in Linux, cloud computing, or high performance computing environments.
Deep experience with meteorological datasets and formats such as NetCDF and GRIB.
Strong Python skills and working knowledge of Fortran, C++, or similar scientific computing languages.
Experience leading technical teams, setting scientific priorities, and communicating complex results to technical and nontechnical audiences.
Ability to manage competing priorities, meet deadlines, and work effectively in a fast-changing startup environment.
Hands-on experience deploying, operating, troubleshooting, or interpreting data from meteorological field equipment.
Experience with Doppler light detection and ranging (lidar) systems, weather radar, radiometers, radiosondes, disdrometers, ceilometers, or surface weather stations.
Experience with field campaigns, environmental monitoring, or deployment operations in the Southwest United States.
Experience at a national laboratory, research university, government weather organization, or artificial intelligence weather company.

Nice To Haves

An advanced degree is preferred.
Experience contributing to WRF development is valuable.

Responsibilities

Set the scientific strategy for evaluating atmospheric conditions, selecting deployment windows, interpreting results, and improving operating criteria.
Integrate forecasts, historical datasets, soundings, radar, satellite imagery, surface observations, and field-sensor data into clear scientific assessments.
Review data before, during, and after deployments. Compare predicted and observed conditions, identify important differences, and communicate conclusions and uncertainty.
Work with science, software, and field teams to improve observation methods, data quality, operational prediction, and post-deployment analysis.
Lead the Weather Research and Forecasting (WRF) modeling program for deployment planning, atmospheric prediction, scenario analysis, and post-deployment review.
Architect WRF data assimilation using WRF Data Assimilation (WRFDA), the Data Assimilation Research Testbed (DART), or similar frameworks.
Integrate standard meteorological observations and data from monitoring and deployment hardware to improve model initial conditions and predictions.
Build an operational modeling chain that ingests historical, forecast, and observational data for high-resolution WRF simulations.
Evaluate artificial intelligence and machine learning weather models and emulators for prediction, model acceleration, and operational decision support.
Develop cloud and high performance computing workflows for model execution, data management, analysis, and visualization.
Set technical priorities and communicate scientific findings, limitations, and uncertainty to company leadership and external research partners.
Recruit, mentor, and lead atmospheric scientists, meteorologists, data scientists, and scientific software engineers as the company grows.
Build productive relationships with universities, national laboratories, research institutions, and other scientific partners.