Senior Geospatial Machine Learning Engineer

Accountabilities:
The Senior Geospatial Machine Learning Engineer will design, develop, and improve machine learning solutions that leverage geospatial data to deliver innovative environmental intelligence products. This role requires strong technical ownership, collaboration, and the ability to translate complex data challenges into practical solutions. Develop new geospatial intelligence products using Python-based geospatial libraries, machine learning, and deep learning techniques. Improve existing solutions through data exploration, model optimization, debugging, and performance enhancements. Work with satellite and aerial imagery, raster and vector datasets, and geospatial workflows to solve real-world challenges. Lead technical projects from planning and experimentation through implementation, delivery, and stakeholder communication. Build tools and processes to evaluate model performance, product impact, and data-driven prioritization. Collaborate with data engineering, product, platform, and delivery teams throughout the full machine learning product lifecycle. Contribute to technical direction, engineering practices, and team culture within a fast-growing environment. Communicate complex technical concepts clearly to both technical and non-technical stakeholders. Requirements: The ideal candidate is an experienced machine learning or geospatial engineer with strong expertise in Python, scientific computing, and applied AI. They should be comfortable working independently, leading projects, and applying advanced technology to environmental and infrastructure challenges. 8–10+ years of experience in machine learning engineering, geospatial engineering, remote sensing, or a closely related technical field. Strong Python programming skills with hands-on experience using geospatial libraries such as GDAL, Rasterio, Shapely, Fiona, and GeoPandas. Experience with scientific Python tools including NumPy, SciPy, scikit-learn, and Pandas. Practical experience developing deep learning solutions using frameworks such as PyTorch and/or TensorFlow. Strong understanding of satellite imagery, aerial imagery, and geospatial raster/vector data processing. Experience with workflow orchestration tools such as Dagster or similar platforms. Ability to independently lead initiatives, manage technical projects, and communicate results effectively. Passion for climate technology and using machine learning to address complex environmental problems. Nice-to-have qualifications: Experience with vegetation science, forestry, energy infrastructure, or utility-related technologies. Familiarity with observability tools such as Sentry and Grafana. Previous experience in climate tech, geospatial AI, remote sensing, or environmental data companies. Benefits: Fully remote work environment with flexibility across eligible locations. Opportunity to work on impactful climate technology projects using AI and satellite data. Ability to influence technical direction, processes, and product development within a growing organization. Collaboration with a diverse international team across engineering, product, design, and platform functions. Exposure to cutting-edge machine learning, geospatial technologies, and real-world applications. Inclusive culture focused on solving meaningful problems through technology. Opportunity for professional growth in a mission-driven environment.

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