Application Guide
How to Apply for Visiting Scientist
at Planet
🏢 About Planet
Planet operates the world's largest constellation of Earth-imaging satellites, providing daily, global coverage that enables unprecedented monitoring of environmental change. Working here means contributing to a mission-driven company that turns data into actionable insights for climate, agriculture, and disaster response.
About This Role
As a Visiting Scientist, you will research and develop a foundation model tailored for Planet's high-cadence, multi-spectral imagery, focusing on time-series analysis and event detection. This role directly impacts Planet's ability to deliver near-real-time insights on floods, fires, and other dynamic phenomena.
💡 A Day in the Life
You'll start by reviewing recent model training runs and debugging issues with Dask clusters. Mid-morning, you might prototype a new contrastive loss function in PyTorch, then spend the afternoon analyzing embedding quality on a flood event dataset. You'll wrap up by documenting findings and syncing with the product team on integration priorities.
🚀 Application Tools
🎯 Who Planet Is Looking For
- A recent PhD who has published in top conferences/journals (e.g., CVPR, IGARSS) on self-supervised learning or remote sensing.
- Deep experience with contrastive learning frameworks (SimCLR, MoCo) applied to satellite or time-series data.
- Proficient in Python and the geospatial stack (xarray, Dask, Rasterio) for handling large-scale raster data.
- Comfortable working in a fast-paced, research-to-product environment and iterating on models based on real-world performance.
📝 Tips for Applying to Planet
Highlight any prior work with Planet imagery or similar high-cadence satellite data (e.g., Sentinel-2 time series).
In your cover letter, explicitly mention experience with foundation models and contrastive learning, referencing specific projects or papers.
Demonstrate familiarity with Planet's APIs (e.g., Planet Data API) by mentioning how you would leverage them for data access.
Showcase your ability to handle cloud cover and temporal gaps—e.g., by describing a multi-sensor fusion project.
Tailor your resume to emphasize impact: use metrics (e.g., improved F1 for flood detection by 15%) rather than just listing tasks.
✉️ What to Emphasize in Your Cover Letter
['Your vision for a Planet-specific foundation model that exploits daily revisit and multi-spectral bands.', 'How you would evaluate and improve existing GFM architectures for PlanetScope data.', 'Your experience building end-to-end workflows for detecting short-lived events (floods, fires) from satellite imagery.', 'Your proficiency with the geospatial Python stack and ability to handle large-scale time-series data.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read Planet's 'Planetary Variables' product descriptions to understand their existing data products.
- → Review recent papers from Planet's research team (e.g., on the Planet Foundation Model) on their blog or arXiv.
- → Familiarize yourself with the Planet Data API and sample notebooks for data access.
- → Study Planet's competitors (Maxar, Satellogic) and how their foundation models differ in approach.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Submitting a generic cover letter that doesn't mention Planet or foundation models specifically.
- Overlooking the time-series aspect—this role is about temporal dynamics, not just static image analysis.
- Failing to demonstrate hands-on experience with geospatial libraries (xarray, Rasterio) in your code samples.
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!