Staff Machine Learning Engineer - Wildfire
New
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OverstoryWildfire modeling
Location: United States; Secondary Locations: Estonia, Sweden, Canada, Ireland, France, Portugal, United Kingdom, Netherlands, Denmark, Switzerland; Workplace: Remote, Eastern North America (NST, AST, EST)Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience designing and building production-grade ML pipelines and systems
- Required Skills
- Machine LearningPyTorchTensorflowDeep LearningComputer Vision
Requirements
- Have 10+ years of experience designing and building production-grade ML pipelines and systems.
- Bring experience at the intersection of machine learning, geospatial data, and environmental science.
- Have a background in wildfire science, forestry, or remote sensing.
- Have a strong background in deep learning, computer vision, or remote sensing.
- Have experience designing end-to-end ML systems, from data ingestion and preprocessing through deployment and monitoring.
- Have hands-on experience with PyTorch, TensorFlow, XGBoost, or LightGBM.
- Have experience with data tools such as Dask, Spark, or GeoPandas.
- Be familiar with GCP and Vertex AI or similar cloud-based ML platforms.
- Be able to lead architectural discussions and mentor other engineers.
- Experience integrating physics-based models with ML, active learning, or uncertainty quantification is a plus.
- Experience with model interpretability, data provenance, or deep learning models for weather or climate data is a plus.
Responsibilities
- Architect and build machine learning models to map and predict vegetation and fuel conditions across 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 evaluation metrics.
- Build reproducible experimentation frameworks and model evaluation workflows.
- Scale models from research to production, focusing on performance, reliability, and explainability.
- Lead the evolution of ML systems, tooling, and processes for wildfire fuelscape models.
- Collaborate with MLOps peers to streamline production training, inference, and monitoring.
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