Staff Machine Learning Engineer - Wildfire

As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation structure, fuel loads, and wildfire risk from satellite and environmental data.

• 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.
• Scale models from research to production with a focus on performance, reliability, and explainability.

This role helps prevent outages, reduce wildfire risks, and accelerate the transition to a safer, more resilient electrical grid by using AI and satellite imagery to pinpoint vegetation risks before they materialize.

• 10+ years of experience designing and building production-grade ML pipelines and systems (6+ years considered for strong candidates).
• 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.
• You are based in the US or Canada.

• Equity and a competitive salary.
• Flexible working environment with a lot of autonomy.
• Remote working budget and educational budget.
• Time to develop new skills.
• Excellent, vibrant, smart team with a culture of openness, tolerance, and respect.

Overstory uses AI and advanced satellite imagery to pinpoint and prioritize vegetation risks before they materialize, helping utilities prevent outages, reduce wildfire risks, and accelerate the transition to a safer, more resilient electrical grid to tackle climate change.