Senior Geospatial Machine Learning Engineer

Join a fully remote, mission-driven climate technology environment where machine learning and satellite imagery are used to address critical infrastructure challenges. As part of the Vegetation Modeling team, you will build advanced ML solutions that identify vegetation-related risks before they contribute to wildfires or power outages. You will work with large-scale geospatial datasets, satellite and aerial imagery, computer vision, and deep learning to create production-ready intelligence products. The role combines hands-on engineering with technical ownership, giving you the opportunity to lead projects from initial planning through delivery. You will collaborate with teams across Europe and the Americas, influencing data pipelines, platform architecture, model evaluation, and product delivery. Your work will directly contribute to improving grid resilience while applying technology to complex environmental and climate challenges. This is an opportunity for a senior ML professional who wants meaningful technical challenges and measurable real-world impact. Accountabilities

• Develop and deploy new vegetation intelligence products using machine learning, deep learning, computer vision, geospatial Python libraries, and large-scale satellite or aerial imagery.
• Explore geospatial datasets, identify opportunities for model improvement, optimize existing ML solutions, and troubleshoot production issues.
• Maintain and enhance existing vegetation modeling products to improve accuracy, reliability, scalability, and overall impact.
• Lead technical projects end-to-end, from defining objectives and planning implementation through execution, delivery, and evaluation.
• Develop measurement frameworks, evaluation tooling, and performance metrics that enable data-driven decisions about model quality and impact.
• Monitor production models and investigate performance issues using appropriate observability, monitoring, and debugging tools.
• Work closely with upstream data ingestion teams to influence data pipelines, processing workflows, and platform architecture.
• Partner with downstream product and delivery teams to ensure geospatial ML outputs can be effectively integrated into customer-facing solutions.
• Use tools such as QGIS, Dagster, Sentry, Grafana, or equivalent platforms to analyze data, manage workflows, monitor systems, and diagnose issues.
• Communicate technical findings, project progress, model performance, and business impact clearly to technical and non-technical stakeholders.
• Contribute to engineering and ML best practices across a distributed team working across Europe and the Americas.
• Help translate complex environmental and geospatial problems into scalable machine learning solutions that support climate resilience and critical infrastructure.

• 5+ years of professional experience as a Machine Learning Engineer, Data Scientist, or in a closely related role, with demonstrated experience building and deploying production machine learning or deep learning models.
• Proven experience developing computer vision or deep learning models using satellite or aerial imagery.
• Strong proficiency in Python and geospatial Python libraries such as rasterio, geopandas, shapely, GDAL, or equivalent technologies.
• Solid understanding of geospatial data structures, formats, processing workflows, and analysis techniques.
• Professional experience with ML and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, or comparable tools.
• Experience designing, implementing, or maintaining data pipelines using orchestration and workflow tools such as Dagster, Airflow, dbt, or equivalent systems.
• Experience with QGIS or comparable geospatial visualization and analysis software.
• Strong understanding of model evaluation, performance measurement, monitoring, and debugging in production environments.
• Ability to work effectively with large-scale, complex datasets and translate technical findings into practical product or business decisions.
• Strong project ownership skills, with the ability to independently drive initiatives from planning through execution and delivery.
• Excellent communication and collaboration skills, particularly in distributed and cross-functional environments.
• Experience with multispectral or hyperspectral satellite imagery is a strong advantage.
• Background in vegetation analysis, forestry, agriculture, environmental monitoring, or related geospatial applications is highly valued.
• Familiarity with observability and monitoring tools such as Grafana, Sentry, Prometheus, or similar platforms is a plus.
• A genuine interest in climate technology, environmental applications, and using advanced technology to solve complex real-world problems is highly desirable.
• Candidates should be comfortable working in a fully remote environment and collaborating across multiple time zones.

• Fully remote working environment.
• Opportunity to work on technology with direct applications in climate action, wildfire prevention, and electrical grid resilience.
• Meaningful ownership of machine learning products and the opportunity to lead projects from concept through production.
• Collaboration with a geographically distributed team spanning Europe and the Americas.
• Exposure to advanced satellite imagery, geospatial data, computer vision, and large-scale machine learning systems.
• High-impact technical challenges involving real-world environmental and infrastructure problems.
• Competitive senior-level compensation package expected, commensurate with experience.
• Opportunity to contribute to the development of production ML systems rather than purely experimental or research-focused models.