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
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Develop new vegetation intelligence products using geospatial Python libraries, machine learning, and deep learning techniques.
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Maintain and improve existing products through data exploration, model optimization, and debugging using tools like QGIS, Dagster, Sentry, and Grafana.
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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.
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Build measurement frameworks and tooling to evaluate model performance and guide data-driven decisions about where to focus impact.
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Collaborate with upstream data ingestion teams and downstream product delivery teams to shape platform architecture and pipelines.
What We're Looking For
Required (dealbreakers):
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5+ years of experience as a Machine Learning Engineer or Data Scientist building and deploying production ML/deep learning models.
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Demonstrated experience building computer vision or deep learning models on satellite or aerial imagery.
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Proficiency with geospatial Python libraries (e.g., rasterio, geopandas, shapely, GDAL) and geospatial data formats.
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Eligible to work without visa sponsorship — no visa sponsorship is available for this role.
Required skills & experience:
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Experience with Python-based ML/deep learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
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Experience with data pipeline orchestration tools (e.g., Dagster, Airflow, dbt) or equivalent workflow management systems.
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Experience with QGIS or equivalent geospatial visualization and analysis software.
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Experience with model monitoring, evaluation metrics, and performance measurement in production environments.
Nice to have:
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Experience working with multi-spectral or hyperspectral satellite imagery data.
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Background in vegetation analysis, forestry, agriculture, or environmental monitoring applications.
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Experience with monitoring and observability tools (e.g., Grafana, Sentry, Prometheus).
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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
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Languages & Libraries: Python, NumPy, SciPy, Pandas, scikit-learn, PyTorch, TensorFlow
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Geospatial: GDAL, rasterio, shapely, fiona, geopandas, QGIS
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Pipelines & Orchestration: Dagster (or similar — Airflow, dbt)
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Monitoring & Observability: Grafana, Sentry, Prometheus
Originally posted on Himalayas