Visiting Scientist (San Francisco Office)
About the role
We are seeking a highly motivated Visiting Scientist (Postdoctoral Researcher) to join our AI Research (AIR) team for a one-year residency. In this role, you will work directly with Dr. Mirela Tulbure during her sabbatical at Planet to develop our proprietary geospatial foundation models (GFMs).
Responsibilities
Contribute to the design and training of a foundation model specifically optimized for Planet imagery, focusing on the integration of time-series data.
Execute the systematic evaluation of existing GFM architectures (e.g., TerraMind, Prithvi, Clay) against PlanetScope data to identify performance bottlenecks and transferability.
Build and test workflows for detecting short-lived events, such as floods and fires, using high-cadence embeddings.
Develop methods to integrate PlanetScope with Sentinel-1 SAR and other commercial datasets to maintain time-series continuity under cloud cover.
Work closely with Planet’s research scientists to transition experimental prototypes into scalable, operational products.
Co-author findings for publication in top-tier journals and present research at leading conferences like IGARSS or CVPR.
Requirements
A recently completed PhD in Geospatial Analytics, Computer Science, Remote Sensing, or a related field.
Demonstrated experience in building AI-based models for environmental change or satellite image analysis.
Hands-on experience with foundation models, contrastive learning, and deep learning frameworks (PyTorch/TensorFlow).
Expert-level Python skills and proficiency with the geospatial scientific stack (e.g., xarray, Dask, Rasterio, GeoPandas).
Experience building automated pipelines for preprocessing and labeling planetary-scale datasets.
Experience working within a research lab environment and a strong desire to apply academic rigor to industry challenges.
Qualifications
Specialized Domain Knowledge: Prior research in flood-extent mapping, water dynamics, or disaster response.
GFM Fine-Tuning: Direct experience fine-tuning or modifying specific GFM architectures like TerraMind, Prithvi, or Clay.
Multi-Sensor Expertise: Proven ability to work with a variety of sensors including PlanetScope, Landsat, and Sentinel-1/2.
Operational Mindset: A history of developing "human-in-the-loop" workflows or active learning strategies for labeling time-sensitive data.
Skills
Academic Foundation: A recently completed PhD in Geospatial Analytics, Computer Science, Remote Sensing, or a related field.
Research Track Record: Demonstrated experience in building AI-based models for environmental change or satellite image analysis.
AI/ML Fluency: Hands-on experience with foundation models, contrastive learning, and deep learning frameworks (PyTorch/TensorFlow).
Advanced Technical Stack: Expert-level Python skills and proficiency with the geospatial scientific stack (e.g., xarray, Dask, Rasterio, GeoPandas).
Data Engineering Aptitude: Experience building automated pipelines for preprocessing and labeling planetary-scale datasets.
Collaborative Research: Experience working within a research lab environment and a strong desire to apply academic rigor to industry challenges.
Benefits
Comprehensive Medical, Dental, and Vision plans
Health Savings Account (HSA) with a company contribution
Generous Paid Time Off in addition to holidays and company-wide days off
16 Weeks of Paid Parental Leave
Wellness Program and Employee Assistance Program (EAP)
Monthly Phone and Internet Reimbursement
Tuition Reimbursement and access to LinkedIn Learning
Equity
Commuter Benefits (if local to an office)
Pay
The US base salary range for this full-time position at the commencement of employment is listed below. Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location.
San Francisco Salary Range: $144,500—$180,600 USD
Schedule
Full-time position
Application Deadline
August 12, 2026 by 11:59p / 23:59 CET (Central European Time)