Head of Atmospheric Research

Roadrunner Venture Studios is recruiting a Head of Atmospheric Research 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. Priorities will change quickly. If this working environment does not excite you, this role is probably not for you.

Atmospheric Science and Prediction

• 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.

Atmospheric Modeling and Data Assimilation

• 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.

Scientific and Team Leadership

• 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.