Senior Geospatial Machine Learning Engineer

Overview

About the Role Join a fast-growing, mission-driven climate tech company using AI and advanced satellite imagery to help electric utilities manage vegetation risks, preventing wildfires, reducing outages, and building a more resilient energy grid. You'll be part of a multidisciplinary Vegetation Modeling team working at the intersection of geospatial data, machine learning, and real-world environm

What you'll do

• About the Role Join a fast-growing, mission-driven climate tech company using AI and advanced satellite imagery to help electric utilities manage vegetation risks, preventing wildfires, reducing outages, and building a more resilient energy grid.
• You'll be part of a multidisciplinary Vegetation Modeling team working at the intersection of geospatial data, machine learning, and real-world environmental impact.
• As a Senior Geospatial Machine Learning Engineer , you'll spend most of your time working within small, focused groups to experiment with, build, and improve algorithms that help understand how vegetation affects utility infrastructure.
• Past work has included co-registering imagery, locating critical energy infrastructure, and identifying tree species, heights, and health.
• Current work spans maintaining and improving these solutions as well as developing new features to assess wildfire risk and evaluate vegetation management strategies.
• What You'll Do Develop new vegetation intelligence products using standard geospatial Python libraries alongside machine learning and deep learning tools.
• Support existing products through data exploration, model improvements, and bug fixes, working regularly with QGIS , Dagster , Sentry , and Grafana.
• Lead projects and initiatives end-to-end: own planning, execution, and delivery, ensuring the value of contributions is clearly communicated to stakeholders across the organisation.
• Build tooling and processes to measure the performance and business value of your team's work, supporting data-driven prioritisation decisions.
• Collaborate closely with upstream data ingestion teams and downstream delivery/refinement teams throughout the full scientific product lifecycle.

Requirements

• Practical experience with Engineering.
• Experience level: 8-10 yrs.
• Strong written and verbal communication in English.
• Comfortable working remotely with distributed teams.

Skills