Full-Time Staff Machine Learning Engineer

• 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.
• Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments.

• 10+ years of experience designing and building production-grade ML pipelines and systems (6+ years considered).
• Strong background in deep learning, computer vision, or remote sensing.
• Skilled in designing end-to-end ML systems.
• 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 ET → PT time zones.

• 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.

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