Develop and train ML models for cloud microphysics, storm evolution, and seeding impact.
Build pipelines for ingesting balloon, radar, satellite, and model data.
Own experiments: preprocessing, training, evaluation, uncertainty.
Convert research into production-ready forecasting tools.
Work closely with atmospheric + hardware teams on data and field integration.
Strong ML research background (spatiotemporal, physics-informed, or time-series).
Experience training large models or working with distributed compute.
Comfortable reasoning from first principles and designing experiments.
Fast, independent, and thrives in a small, high-velocity team.
Passion for climate/weather.
Bonus: WRF, LES, data assimilation, microphysics knowledge.
Skills Required
- Strong machine learning research background in spatiotemporal, physics-informed, or time-series modeling
- Experience training large models or working with distributed compute
- Ability to reason from first principles and design experiments
- Ability to work independently in a fast-paced, high-velocity team
- Passion for climate and weather
- Experience with WRF, LES, data assimilation, or microphysics
What We Do
Recast Systems is a weather-technology company developing an integrated system for precision weather modification. It currently uses cloud seeding and aims to help ease drought, floods, and extreme weather. Its approach combines traceable biological seeding materials, atmospheric sensors and other hardware, AI weather simulations, and field operations. By feeding measurements and mission results back into its models, Recast seeks to quantify cloud-seeding impacts and make each operation more precise.
