Staff Machine Learning Engineer - Wildfire

Overstory uses AI and advanced satellite imagery to help utilities prevent outages, reduce wildfire risks, and build a more resilient electrical grid. The Staff Machine Learning Engineer will lead the development and scaling of the Wildfire Fuel Detection Model, architect geospatial ML systems, and guide models from experimentation through production while mentoring engineers and driving technical standards.

Responsibilities:

• 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

Requirements:

• • Experience thriving at the intersection of machine learning, geospatial data, and environmental science; deeply motivated by the opportunity to reduce wildfire risk through data-driven insights
• • 10+ years of experience designing and building production-grade ML pipelines and systems
• • You have a background in wildfire science, forestry, or remote sensing
• • Strong background in deep learning, computer vision, or remote sensing
• • Skilled in designing end-to-end ML systems — from data ingestion and preprocessing to deployment and monitoring
• • Hands-on experience with frameworks like PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas
• • Familiarity with GCP and Vertex AI, or similar cloud-based ML platforms
• • Strong communication skills and ability to collaborate across technical and scientific domains
• • Comfortable leading architectural discussions and mentoring other engineers
• • Background in wildfire science, forestry, or remote sensing
• • Experience integrating physics-based models with ML or working with active learning and uncertainty quantification
• • Experience in model interpretability and data provenance for environmental ML systems
• • Experience with deep learning models for weather or climate data
• • Experience in remote-first or globally distributed teams