About the Role
Join a fast-growing, mission-driven climate tech company using AI and advanced satellite imagery to help electric utilities manage vegetation risks — preventing wildfires, reducing outages, and building a more resilient energy grid. You'll be part of a multidisciplinary Vegetation Modeling team working at the intersection of geospatial data, machine learning, and real-world environmental impact.
As a Senior Geospatial Machine Learning Engineer, you'll spend most of your time working within small, focused groups to experiment with, build, and improve algorithms that help understand how vegetation affects utility infrastructure. Past work has included co-registering imagery, locating critical energy infrastructure, and identifying tree species, heights, and health. Current work spans maintaining and improving these solutions as well as developing new features to assess wildfire risk and evaluate vegetation management strategies.
What You'll Do
Develop new vegetation intelligence products using standard geospatial Python libraries alongside machine learning and deep learning tools.
Support existing products through data exploration, model improvements, and bug fixes — working regularly with QGIS, Dagster, Sentry, and Grafana.
Lead projects and initiatives end-to-end: own planning, execution, and delivery, ensuring the value of contributions is clearly communicated to stakeholders across the organisation.
Build tooling and processes to measure the performance and business value of your team's work, supporting data-driven prioritisation decisions.
Collaborate closely with upstream data ingestion teams and downstream delivery/refinement teams throughout the full scientific product lifecycle.
Contribute to shaping team culture, processes, and technical direction at an early-stage, high-growth company.
What We're Looking For
Required
8–10+ years of relevant experience in machine learning engineering, geospatial engineering, or a closely related field.
Strong proficiency in Python, with hands-on experience using geospatial libraries: GDAL, Rasterio, Shapely, Fiona, GeoPandas.
Experience with scientific Python tools: NumPy, SciPy, scikit-learn, Pandas.
Practical experience with deep learning frameworks: PyTorch and/or TensorFlow.
Comfortable working with satellite or aerial imagery and raster/vector geospatial datasets.
Experience with workflow orchestration tools (e.g. Dagster or similar).
Ability to lead projects independently and communicate technical work clearly to non-technical stakeholders.
Passion for climate action and solving complex environmental challenges through technology.
Nice to Have
Familiarity with vegetation science, forestry, or utility/energy infrastructure domains.
Experience with observability tooling such as Sentry or Grafana.
Background working at a climate tech, geospatial AI, or remote sensing company.
Location & Work Arrangement
Fully remote — primary hiring location is Portugal, with team members also based across Europe and the Americas.
Visa sponsorship is not available for this role.
About the Team & Culture
The team is 15+ nationalities strong and includes outdoor enthusiasts, musicians, artists, athletes, and adventurers. What brings everyone together is a deep commitment to solving complex problems and using technology as a force for good. You'll work cross-functionally with product, design, engineering, and platform teams — and have a genuine opportunity to influence the direction and culture of the organisation as it scales.
Clera is building autonomous AI agents that handle end-to-end recruiting operations. The company's AI agents find candidates, write personalized outreach, handle conversations, qualify fit, and schedule interviews without human intervention. Clera has raised pre-seed funding from top investors and has grown to marketplace scale with 40,000+ talents and hundreds of top startup customers. The company is now scaling its AI-native recruiting platform by hiring operators who supervise the agents, handle high-judgment moments, and close candidates that require human touch.
