Senior Geospatial Machine Learning Engineer
About the Role
Join the Vegetation Modeling team at a mission-driven climate-tech company that uses AI and advanced satellite imagery to help utilities prevent wildfires and power outages by identifying vegetation risks before they become critical. As a Senior Geospatial Machine Learning Engineer, you'll develop and improve ML solutions that analyze geospatial data and satellite imagery — making a direct, measurable impact on grid resilience and climate action. The team spans the Americas and Europe, and this role is fully remote.
What You'll Do
Develop new vegetation intelligence products using geospatial Python libraries, machine learning, and deep learning techniques.
Maintain and improve existing products through data exploration, model optimization, and debugging using tools like QGIS, Dagster, Sentry, and Grafana.
Lead projects end-to-end — from planning and execution through delivery — and communicate the value of your work to cross-functional stakeholders throughout the organization.
Build measurement frameworks and tooling to evaluate model performance and guide data-driven decisions about where to focus impact.
Collaborate with upstream data ingestion teams and downstream product delivery teams to shape platform architecture and pipelines.
What We're Looking For
Required (dealbreakers):
5+ years of experience as a Machine Learning Engineer or Data Scientist building and deploying production ML/deep learning models.
Demonstrated experience building computer vision or deep learning models on satellite or aerial imagery.
Proficiency with geospatial Python libraries (e.g., rasterio, geopandas, shapely, GDAL) and geospatial data formats.
Eligible to work without visa sponsorship — no visa sponsorship is available for this role.
Required skills & experience:
Experience with Python-based ML/deep learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
Experience with data pipeline orchestration tools (e.g., Dagster, Airflow, dbt) or equivalent workflow management systems.
Experience with QGIS or equivalent geospatial visualization and analysis software.
Experience with model monitoring, evaluation metrics, and performance measurement in production environments.
Nice to have:
Experience working with multi-spectral or hyperspectral satellite imagery data.
Background in vegetation analysis, forestry, agriculture, or environmental monitoring applications.
Experience with monitoring and observability tools (e.g., Grafana, Sentry, Prometheus).
Track record of leading cross-functional projects or initiatives from planning through delivery.
Location & Work Arrangement
This is a fully remote role. The team operates across multiple time zones in the Americas and Europe. Candidates based in Canada are preferred for this posting.
⚠️ Visa sponsorship is not available. Applicants must be authorized to work in their country of residence.
Tech Stack
Languages & Libraries: Python, NumPy, SciPy, Pandas, scikit-learn, PyTorch, TensorFlow
Geospatial: GDAL, rasterio, shapely, fiona, geopandas, QGIS
Pipelines & Orchestration: Dagster (or similar — Airflow, dbt)
Monitoring & Observability: Grafana, Sentry, Prometheus
Originally posted on