Location: San Francisco Bay Area (remote optional)

Company: Convective

About the Company

Convective is an early stage startup building upon decades of scientifically validated research to pioneer a novel, paradigm-shifting method of precipitation enhancement and weather modification. Our mission is to unlock abundance: ending droughts, suppressing wildfires, greening deserts, and cooling cities by generating precipitation where the world needs it most.

The Role

We are seeking a Head of Atmospheric Modeling to lead the development of our predictive operational engine.

You will build the bridge between theoretical validation and operational reality, implementing state of the art numerical and AI/ML weather modeling into a cloud-native environment for real-time data assimilation from monitoring and deployment hardware to enable predictive decision-making for operational deployments.

You are likely a senior scientist-engineer coming out of NCAR, NOAA, DOE, a national lab, a leading research university, or an AI weather modeling company with deep experience in numerical weather prediction (NWP) and/or next-generation AI/ML weather models and emulators. You function as both a lead architect and a hands-on builder.

As we are a deployment-focused company, you will need to be excited not just about research and modeling for its own sake, but for the purposes of enabling the next generation of climate adaptation through weather modification and geoengineering deployed within ethical and legal guidelines.

This role includes a significant equity opportunity.

Key Responsibilities

Operational Forecasting & Data Assimilation

• System Architecture: Architect and lead the implementation of a proprietary data assimilation system (using frameworks like WRFDA or DART) on cloud infrastructure (AWS HPC).
• Predictive Tooling: Develop the operational modeling chain that ingests historic and observational datasets for high-resolution NWP simulations (WRF, MPAS) for evaluating deployment use cases.
• AI/ML Emulation: Translate complex atmospheric dynamics into fast, actionable inference models (e.g., using FGNs, Diffusion, or Transformer-based architectures) that guide live deployment decisions.

Strategic Modeling Leadership

• Multi-Scale Hybrid Strategy: Lead the architecture of a tiered modeling stack that fuses physics simulations with AI inference across spatial scales. This ranges from Large Eddy Simulation (LES) for modeling plume dynamics and cloud physics; to WRF and AI/ML mesoscale forecasting for predictive modeling; up to Global Climate Models (CESM) to assess long-term deployment opportunities and macro-scale system impacts.
• External Integration: Build pipelines that integrate with leading external AI weather providers (e.g., WindBorne Systems, Atmo) to guide our deployments.
• Team Building: Recruit, mentor, and lead a small, world-class team of meteorologists, data scientists, and atmospheric physicists.

Ideal Qualifications