Staff ML Engineer: Wildfire Risk & Geospatial AI Lead

Overstory; San Diego (CA); Remote; USD 180,000 - 280,000; Full time

Job summary

Overstory is seeking a Staff Machine Learning Engineer to lead the development and scaling of the Wildfire Fuel Detection Model. You will shape modeling capabilities by leveraging large-scale geospatial data and cutting-edge ML techniques, influencing production systems from data ingestion to deployment.

You will mentor engineers, drive architectural decisions, and establish standards for modeling, experimentation, and deployment with a strong emphasis on reliability and explainability.

Qualifications

• 10+ years of experience designing and building production-grade ML pipelines and systems.

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

Skills

ML pipelines

Geospatial data

Deep learning

PyTorch

TensorFlow

XGBoost/LightGBM

GCP Vertex AI

Mentoring

Communication

Education

Graduate degree in CS/ML/Geospatial

Tools

GeoPandas

Dask

Spark

Kubernetes

Vertex AI

GCP

Job description

Overstory is seeking a Staff Machine Learning Engineer to lead the development and scaling of the Wildfire Fuel Detection Model. You will shape modeling capabilities by leveraging large-scale geospatial data and cutting-edge ML techniques, influencing production systems from data ingestion to deployment.

You will mentor engineers, drive architectural decisions, and establish standards for modeling, experimentation, and deployment with a strong emphasis on reliability and explainability.