Note: The job is a remote job and is open to candidates in USA. Overstory is addressing the climate crisis through innovative technology to enhance the resilience of the electrical grid. As a Staff Machine Learning Engineer, you will lead the development of the Wildfire Fuel Detection Model, working closely with various teams to ensure accuracy and robustness of wildfire models while mentoring other engineers.
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
Skills
- 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
- 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
Benefits
- Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
- Home office stipend, coworking and ongoing education budgets
- A company culture that genuinely embodies each of our core values
- To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis
Company Overview