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Salary: EUR 59,200 - 74,000 / annual
Planet designs, builds, and operates the largest constellation of imaging satellites in history, delivering empirical Earth observation data via a cloud-based platform to commercial, environmental, and humanitarian sectors. You will join the team developing high-quality, validated markers that extract insights from dense temporal stacks of satellite imagery across agriculture, land management, and climate.
In this role, you will own the development of algorithms and machine learning models that extract insights from satellite imagery time series, maintaining and improving existing markers. You will take research topics from exploration through to production, owning marker results for production Area Monitoring System (AMS) deliveries across several EU countries—running them, reviewing them, and confirming quality before they reach clients. You will explore and build embeddings-based insight extraction in new areas beyond the Common Agricultural Policy (CAP), use AI agents in your daily work, and help the team improve at it.
You will co-own the markers codebase with the rest of the team, collaborate iteratively to build solutions on the platform and existing data building blocks, and inspire partners to extract insights using them. You will document and organize your work to be transparent and repeatable, and write internal research reports, public blog posts, and reports for clients.
This is a full-time hybrid role requiring 3 days per week in the Haarlem, Graz, or Ljubljana office.
REQUIREMENTS:
- 4+ years of relevant work experience
- Bachelor's degree or higher in computer science, data science, or another STEM field
- Understanding of machine learning principles, including model validation and performance evaluation techniques
- Proficiency in Python programming with ability to write maintainable, well-documented code
- Experience working with AI agents and modern machine learning tools
- Experience with version control and Git, and comfort working in a shared codebase
- Working knowledge of the geospatial domain
- Ability to deliver projects on schedule and manage technical deliverables
- Problem-solving skills in technical or analytical domains
- Professional working proficiency in English
DESIRABLE:
- Experience working with embeddings or other learned representations of imagery
- Remote sensing expertise, particularly with satellite time series
- Experience processing radar data (e.g., Sentinel-1)
- Familiarity with the agricultural domain or EU Common Agricultural Policy and area-based payment schemes
- Experience with cloud environments and distributed computing
