Staff Machine Learning Engineer - Wildfire

Overview This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff Machine Learning Engineer - Wildfire in the United States and Canada . This role offers the chance to lead cutting-edge machine learning initiatives that reduce wildfire risk and protect communities. You will develop and scale advanced ML models that analyze satellite and environmental data to predict vegetation and fuel conditions. The position involves designing robust data pipelines, building reproducible experimentation frameworks, and translating scientific insights into production-ready ML systems. You will collaborate with data scientists, ML engineers, and domain experts while mentoring peers and guiding architectural decisions. Your work will have a direct impact on wildfire prevention, grid resilience, and climate risk mitigation across diverse geographies. Responsibilities Architect, develop, and maintain ML models for vegetation mapping and wildfire fuel detection Design and manage scalable data and feature pipelines for large-scale geospatial and temporal datasets Collaborate with wildfire science and product teams to define modeling goals, evaluation metrics, and real-world impact Build reproducible experimentation frameworks and evaluation workflows to ensure scientific rigor Scale models from research to production, prioritizing performance, reliability, and explainability Drive architectural decisions, tooling improvements, and process evolution for maintainable ML systems Mentor and provide technical leadership to other engineers, promoting best practices in modeling and deployment Qualifications 6+ years of experience in machine learning engineering, with preference for 10+ years in production-grade ML systems Strong expertise in deep learning, computer vision, or remote sensing applied to geospatial data Skilled in end-to-end ML systems design, including data ingestion, preprocessing, model training, deployment, and monitoring Hands-on experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas Familiarity with cloud ML platforms (e.g., GCP, Vertex AI) and large-scale distributed infrastructure Proven ability to collaborate across technical and scientific teams, communicate complex concepts, and lead architectural discussions Based in the United States or Canada Nice-to-Haves Background in wildfire science, forestry, or environmental modeling Experience with physics-based models, active learning, or uncertainty quantification Knowledge of model interpretability and data provenance for environmental ML systems Experience with deep learning for weather or climate data Experience working in remote-first or globally distributed teams Benefits Competitive salary and equity opportunities Flexible, remote-first work environment with autonomy and support for in-person collaboration as needed Educational and professional development budget Access to cutting-edge technology and tools for ML research and production Collaborative, mission-driven team culture emphasizing openness, respect, and diversity Opportunities to contribute directly to wildfire risk reduction and climate resilience #J-18808-Ljbffr