Geospatial Machine Learning Engineer

Position: Staff Geospatial Machine Learning Engineer
Senior Geospatial Machine Learning Engineer

Join to apply for the Senior Geospatial Machine Learning Engineer role at Overstory

The climate crisis is the defining challenge of our time—but it’s also the greatest opportunity for innovation. At Overstory, we’re harnessing cutting‑edge technology to enable a resilient electrical grid that keeps communities thriving as our world changes.

The grid is the backbone of life as we know it. It powers hospitals, keeps food fresh, and ensures communities stay connected. Extreme weather, aging infrastructure, and growing wildfire risks are putting this critical system under pressure. The electric utility industry presents the greatest opportunity for tackling climate change.

One of the leading causes of catastrophic wildfires and power outages is trees and brush coming into contact with power lines. At Overstory, we use AI and advanced satellite imagery to pinpoint and prioritize vegetation risks before they materialize. By giving utilities critical analysis on those risks, we’re helping prevent outages, reduce wildfire risks, and accelerate the transition to a safer, more resilient grid.

Our team spans the Americas and Europe, and we work with utility partners across the region. We’re outdoor enthusiasts, musicians, artists, athletes, parents, and adventurers—15 nationalities strong and growing. What unites us is a passion for solving complex problems, a commitment to climate action, and the belief that technology should be a force for good. Join us to help build a more resilient world together.

We are looking for a Senior Geospatial MLE to join the Vegetation Modeling team ’ll spend time in small groups experimenting with, building, and improving algorithms to understand how vegetation impacts our customers. We’ve developed solutions to coregister imagery, locate critical energy infrastructure, and identify tree species, heights, and health. Currently, we split our time between maintaining and improving these solutions and developing new features to understand wildfire risk and assess the impact of management strategies.

On the team, you’ll work with satellite imagery and geospatial datasets, using machine learning or deep learning to analyze their contents. You’ll regularly interface with data and models through Python code, QGIS projects, and Dagster pipelines. You’ll thrive when collaborating with a cross‑functional group of product, design, engineering, and platform team members, and being involved throughout the full scientific product lifecycle.

As a quickly growing, early‑stage startup, you’ll contribute to the direction and culture of our organization. We collaborate closely with upstream teams responsible for data ingestion and downstream teams that deliver outputs to customers. You’ll help shape how we work within our team and across the organization, influence core platform and pipeline decisions, and scope impactful projects.

What You’ll Do

• Develop new vegetation intelligence products using standard geospatial Python libraries, as well as machine learning and deep learning tools

• Support existing products through data exploration, model improvements, and bugfixes, especially using QGIS, Dagster, Sentry, and Grafana

• Represent the team by leading projects and initiatives, planning, execution, delivery, and ensuring the value of our contributions is clear to stakeholders across the organization

• Build tooling and processes to measure value and performance of contributions, and help make data‑driven decisions about where we can have the greatest impact

About You

• At least 8 years of commercial experience as a Data Scientist or Machine Learning Engineer

• Advanced scientific Python experience, including numpy, scipy, scikit-learn, pandas

• Experience with geospatial or satellite data, and tooling such as gdal, rasterio, shapely, fiona, geopandas, QGIS

• Experience with deep learning algorithms applied to geospatial data, and tooling such as pytorch, tensor flow

• Experience leading small teams organized around projects or initiatives

• Passionate about climate

• Enjoy working in a remote‑first, fast‑moving environment

• Based in GMT/ CET/ EST time zones

Nice‑t…