Staff Machine Learning Engineer - Wildfire

About the role

The Staff Machine Learning Engineer position at Overstory is dedicated to developing and scaling the Wildfire Fuel Detection Model. This model is crucial for understanding vegetation structure, fuel loads, and wildfire risk from satellite and environmental data. The role involves architecting and building advanced ML models, designing and maintaining robust data and feature pipelines, and collaborating with wildfire science and product teams to ensure the accuracy and reliability of the models.

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 to ensure our wildfire fuelscape models remain state-of-the-art and maintainable.
• Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments.

Requirements

• 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.

Qualifications

• Experience in wildfire science, forestry, or remote sensing is a plus.
• Experience integrating physics-based models with ML or working with active learning and uncertainty quantification is a plus.
• Experience in model interpretability and data provenance for environmental ML systems is a plus.
• Experience with deep learning models for weather or climate data is a plus.
• Experience in remote-first or globally distributed teams is a plus.

Skills

• Experience with machine learning, geospatial data, and environmental science.
• 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.
• 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.

Benefits

Competitive, location-specific compensation and benefits.

A flexible, autonomous, and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around.

A 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.

Pay

Location-specific compensation and benefits.

Schedule

Flexible schedule.

Note

We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.