Senior Geospatial Machine Learning Engineer
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Job Description
Senior Geospatial ML Engineer building satellite-imagery vegetation intelligence for a climate-tech company. Improving production models and pipelines to help utilities prevent wildfires and outages.
Responsibilities:
- 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
- Lead projects end-to-end from planning and execution through delivery
- Communicate the value of work to cross-functional stakeholders across the organization
- Build measurement frameworks and tooling to evaluate model performance
- 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
Requirements:
- 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 such as rasterio, geopandas, shapely, and GDAL
- Proficiency with geospatial data formats
- Eligible to work without visa sponsorship; no visa sponsorship is available
- Experience with Python-based ML/deep learning frameworks such as PyTorch, TensorFlow, and scikit-learn
- Experience with data pipeline orchestration tools such as Dagster, Airflow, or 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
- Experience with multi-spectral or hyperspectral satellite imagery data is nice to have
- Background in vegetation analysis, forestry, agriculture, or environmental monitoring applications is nice to have
- Experience with monitoring and observability tools such as Grafana, Sentry, or Prometheus is nice to have
- Track record of leading cross-functional projects or initiatives from planning through delivery is nice to have
Benefits:
- Fully remote work arrangement
- Opportunity to make a direct, measurable impact on grid resilience and climate action
- Mission-driven climate-tech work focused on wildfire prevention and power outage prevention
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