• Architect and build advanced ML models to map and predict vegetation and fuel conditions across diverse geographies. • Design and maintain robust data and feature pipelines for large-scale geospatial and temporal data. • Partner with wildfire science and product teams to define modeling objectives and evaluation metrics tied to real-world impact. • Build reproducible experimentation frameworks and model evaluation workflows. • Scale models from research to production with a focus on performance, reliability, and explainability. • Lead the evolution of ML systems, tooling, and processes — ensuring that our wildfire fuelscape models remain state-of-the-art and maintainable. • Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments.