Planet
Visiting Staff Scientist
San Francisco, CA · Staff+ · Full-time
Sponsorship not specified$232k-$289kDetected 71 days ago
PythonMachine LearningDeep LearningNumPyData EngineeringLLMsCadenceHardware DesignSensorsResearchLeadershipRemote Sensing
About the role
- We are seeking a distinguished Visiting Staff Scientist to join our AI Research (AIR) team for a one-year sabbatical residency.
- Benchmark Geospatial Architectures: Systematically evaluate and compare existing GFMs (e.g., TerraMind, Prithvi, Clay) against PlanetScope data to assess performance, computational cost, and transferability.
- Multi-Sensor Integration: Explore the synergy between PlanetScope, Sentinel-1 SAR, and other commercial SAR data to ensure robust time-series analysis even under cloud cover.
Responsibilities
- Lead the research and development of a foundation model specifically trained on Planet imagery, incorporating the time-axis to create high-cadence time-series embeddings.
- Design embeddings and workflows optimized for detecting short-lived, high-impact events such as floods, rapid surface-water expansion, and fire.
- Oversee the technical direction of a dedicated postdoc and collaborate with Planet's research scientists to transition prototypes into operational products.
- You will collaborate with a multi-disciplinary team of "Planeteers" across space operations, data pipelines, and analytics to co-develop AI/ML solutions that leverage the high spatial resolution and near-daily revisit of PlanetScope data.
- Develop Planet's Proprietary GFM: Lead the research and development of a foundation model specifically trained on Planet imagery, incorporating the time-axis to create high-cadence time-series embeddings.
- Capture Dynamic Earth Events: Design embeddings and workflows optimized for detecting short-lived, high-impact events such as floods, rapid surface-water expansion, and fire.
- Mentor & Collaborate: Oversee the technical direction of a dedicated postdoc and collaborate with Planet's research scientists to transition prototypes into operational products.
Requirements
- Advanced Geospatial Toolkit: Proficiency in multi-sensor integration (Landsat, Sentinel-2, PlanetScope, Sentinel-1) and high-resolution mapping at varying scales (3m, 10m, 30m).
- Proven Funding & Publication Record: History of leading NASA-funded or similar high-impact geospatial research projects.
- Distinguished Academic Background: PhD and current Faculty/Professor status in Geospatial Analytics, Computer Science, Remote Sensing, or a related field.
- Deep Domain Expertise: 12+ years of experience in remote sensing and satellite image analysis, with a proven track record in building AI-based models for environmental change (e.g., flood-extent, water dynamics).
- Multimodal AI Fluency: Extensive experience with foundation models, contrastive learning (CLIP-like models), and multi-model vision-language models (MMVLMs).
- Technical Proficiency: Expert-level Python skills and experience with the scientific stack (xarray, Dask, NumPy, Rasterio, GeoPandas) and deep learning frameworks.
- Scale-Minded Research: Experience building automated pipelines for preprocessing and labeling planetary-scale datasets.
- Collaborative Spirit: A history of leading research labs and a desire to work in a fast-paced, industrial R&D environment.
- What Makes You Stand Out:
- Specialized Environmental Research: Extensive experience specifically in flood damage quantification and methane-related water dynamics.
- Architectural Knowledge: Direct experience fine-tuning or modifying specific GFM architectures like TerraMind or Prithvi.
- Hybrid Experience: A mix of deep academic rigor and the ability to prototype rapid-change monitoring tools for operational readiness.
Skills
- Welcome to Planet.
Compensation
- 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
Benefits
- Extensive experience with foundation models, contrastive learning (CLIP-like models), and multi-model vision-language models (MMVLMs).
- 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
Company info
- In this role, you will play a pivotal part in our mission to create a "Queryable Earth" by leading the development of Planet's proprietary geospatial foundation models (GFMs).
- While Planet has historically leveraged external models like Google's RSFM and RemoteCLIP, we are now focused on building in-house models specifically trained on our unique imagery.
Equal opportunity
- 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.
- Know Your Rights.
- EEO statement:
Visa & Work Authorization
- 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.