4 months ago
Remote, United States or Toronto, CanadaStaff+
Responsibilities
- Architect and build machine learning models that map and predict vegetation and fuel conditions across diverse geographies.
- Design and maintain data and feature pipelines for large-scale geospatial and temporal data.
- Partner with wildfire science and product teams to define modeling objectives and real-world evaluation metrics.
- Build reproducible experimentation frameworks and model evaluation workflows.
- Scale models from research to production with emphasis on performance, reliability, and explainability.
- Lead the evolution of ML systems, tooling, processes, and modeling standards.
- Collaborate with MLOps peers on production training, inference, and monitoring.
- Drive architectural decisions and mentor other engineers.
Requirements
- 10+ years of experience designing and building production-grade machine learning pipelines and systems.
- Experience across machine learning, geospatial data, and environmental science.
- Strong background in deep learning, computer vision, or remote sensing.
- Experience designing end-to-end ML systems from data ingestion and preprocessing through deployment and monitoring.
- Hands-on experience with PyTorch, TensorFlow, XGBoost, LightGBM, Dask, Spark, or GeoPandas.
- Familiarity with GCP and Vertex AI or similar cloud-based ML platforms.
- Strong communication and cross-functional collaboration skills.
- Ability to lead architectural discussions and mentor engineers.
- Preferred experience in wildfire science, forestry, or remote sensing.
- Preferred experience integrating physics-based models with ML, active learning, or uncertainty quantification.
- Preferred experience with model interpretability, data provenance, and deep learning models for weather or climate data.
- Experience in remote-first or globally distributed teams is a plus.
Benefits
- Remote work from one of the listed countries: United States, the Netherlands, United Kingdom, Ireland, Estonia, Portugal, France, Sweden, Switzerland, Denmark, or Canada.
- Eastern North America time zone requirement.
- Home office stipend, coworking budget, and ongoing education budget.
- Flexible, autonomous, and collaborative work environment.
- Annual in-person team gathering with occasional in-person collaboration.
- Competitive, location-specific benefits.
Tech Stack
Categories
Data EngineeringML Engineering
About Overstory
At Overstory, we're building AI-powered grid resilience software to support the energy transition. We combine satellites and machine learning to show utilities where they need to mitigate critical vegetation risk, helping them prevent outages, reduce threat of wildfire, and provide safer, more reliable power.