Planet

Visiting Scientist (San Francisco Office)

San Francisco, CA · Full-time

Sponsorship not specified$145k-$181kDetected 50 days ago

PythonMachine LearningDeep LearningTensorFlowPyTorchData EngineeringLLMsCadenceHardware DesignResearchRemote Sensing

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.
As a postdoctoral researcher, you will be the primary technical engine behind creating temporally dense embeddings that capture the dynamic and ephemeral nature of our planet-such as rapid flooding and disaster impacts.

Contribute to the design and training of a foundation model specifically optimized for Planet imagery, focusing on the integration of time-series data.
Build and test workflows for detecting short-lived events, such as floods and fires, using high-cadence embeddings.
Mirela Tulbure during her sabbatical at Planet to develop our proprietary geospatial foundation models (GFMs).
You will collaborate with "Planeteers" across data pipelines and analytics to bridge the gap between academic research and operational AI/ML solutions.
GFM Implementation: Contribute to the design and training of a foundation model specifically optimized for Planet imagery, focusing on the integration of time-series data.
Prototype Development: Build and test workflows for detecting short-lived events, such as floods and fires, using high-cadence embeddings.

Advanced Technical Stack: Expert-level Python skills and proficiency with the geospatial scientific stack (e.g., xarray, Dask, Rasterio, GeoPandas).
Multi-Sensor Expertise: Proven ability to work with a variety of sensors including PlanetScope, Landsat, and Sentinel-1/2.
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).
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.
What Makes You Stand Out:
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.
Operational Mindset: A history of developing "human-in-the-loop" workflows or active learning strategies for labeling time-sensitive data.
Application Deadline:

The US base salary range for this full-time position at the commencement of employment is listed below.
The final salary range is determined by job related experience, skills and location.
The range displays our typical hiring range for new hire salaries in US locations only.
Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
San Francisco Salary Range
San Francisco Fair Chance Ordinance

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)
Home Office Reimbursement
Monthly Phone and Internet Reimbursement
Tuition Reimbursement and access to LinkedIn Learning
Commuter Benefits (if local to an office)
Volunteering Paid Time Off
Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity).

While Planet has historically leveraged external models, we are now focused on building in-house models specifically trained on our unique imagery.

Planet is committed to building a community where everyone belongs and we invite people from all backgrounds to apply.
Planet is an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws.
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EEO statement:

Should an interview involve use of AI interview technologies, the candidate will receive notification and have the ability to opt out both in advance and/or real-time.

This listing is sourced directly from Planet's careers page and normalized into a canonical job model.