About this role
Adaptable professionals who are curious about AI agents and eager to iterate directly based on customer feedback will thrive in this position. Planet operates the largest constellation of imaging satellites in history, combining space technology and data processing to deliver empirical information to commercial, environmental, and humanitarian sectors worldwide.
The engineering team builds high-quality, validated markers that extract insights from dense temporal stacks of satellite imagery for agriculture, land management, and climate. Daily responsibilities include developing machine learning models, taking research topics from exploration through to production, and maintaining the Area Monitoring System delivered to European paying agencies. Team members co-own the markers codebase, utilize AI agents in daily tasks, and collaborate closely with scientists to deploy models at scale.
This hybrid role exists to advance the company's agricultural and environmental monitoring capabilities while expanding into land-cover change detection using embeddings and AI-first workflows. Successful candidates bring solid programming foundations and a readiness to manage technical deliverables within a shared codebase.
What you will do
- Develop algorithms and machine learning models that extract insights from satellite imagery time series while maintaining existing markers.
- Take research topics from initial exploration through to production deployment.
- Own marker results for production Area Monitoring System deliveries across several European countries by running, reviewing, and confirming quality.
- Explore and build embeddings-based insight extraction in new areas beyond the Common Agricultural Policy.
- Incorporate AI agents into daily work and help the team improve related workflows.
- Co-own the markers codebase with other team members and collaborate to build solutions on existing data building blocks.
- Document and organize work transparently, writing internal research reports, public blog posts, and client reports.
What you need
- Four or more years of relevant work experience in technical or analytical domains.
- 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 the ability to write maintainable, well-documented code.
- Experience working with AI agents and modern machine learning tools.
- Experience with version control and Git, along with comfort working in a shared codebase.
- Working knowledge of the geospatial domain and professional working proficiency in English.
Nice to have
- Experience working with embeddings or other learned representations of imagery
- Remote sensing expertise, particularly with satellite time series
- Experience processing radar data such as Sentinel-1
- Familiarity with the agricultural domain or the EU Common Agricultural Policy and area-based payment schemes
- Experience with cloud environments and distributed computing
Benefits
- Paid time off including vacation, holidays and company-wide days off
- Employee Wellness Program
- Home Office Reimbursement
- Monthly Phone and Internet Reimbursement
- Tuition Reimbursement and access to LinkedIn Learning
- Equity and Volunteering Paid Time Off
Skills and tech stack
Questions candidates ask
What is the work location and remote policy?
This is a full-time, hybrid role which will require you to work from the Graz, Ljubljana or Haarlem office 3 days per week. Planet is a global company with employees working remotely worldwide and joining from various international offices.
What is the compensation and salary range for this role?
The expected starting gross salary range for the Slovenia location is €45.600 to €57.000 EUR. Individual placement within this range is determined objectively based on criteria such as skills, qualifications, and professional experience.
What tools and technologies will I be working with?
The role involves Python programming, version control with Git, AI agents, modern machine learning tools, and multi-sensor Earth observation data including satellite imagery time series and radar data.
What does the team actually build?
The team develops high-quality, validated markers that extract insights from dense temporal stacks of satellite imagery across agriculture, land management, and climate. These markers form the core of the Area Monitoring System delivered to Common Agricultural Policy paying agencies across Europe.