Planet

space

VisitingStaffScientist

$232–289k San Francisco, California, United States FULL TIME
The Brief

“Visiting Staff Scientist at Planet. Skills: geospatial foundation models (GFMs), time-series embeddings, contrastive learning, multi-model vision-language models (MMVLMs), multi-sensor integration, Python, deep learning frameworks. 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. Systematically evaluate and compare existing GFMs against PlanetScope data to assess performance,”

What You'll Achieve.

create a “Queryable Earth”; Develop Planet’s Proprietary GFM; Benchmark Geospatial Architectures; Capture Dynamic Earth Events; Multi-Sensor Integration; Human-in-the-Loop Innovation; Academic & Technical Leadership; Mentor & Collaborate; transition prototypes into operational products

Industry & Context.

space
Problems you'll solve

solve our world’s toughest obstacles; building AI-based models for environmental change; design embeddings and workflows optimized for detecting short-lived, high-impact events; explore the synergy between PlanetScope, Sentinel-1 SAR, and other commercial SAR data to ensure robust time-series analysis even under cloud cover; design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks

Eligibility Requirements

one-year sabbatical residency

What They're Looking For.

Must Have

PhD and current Faculty/Professor status in Geospatial Analytics, Computer Science, Remote Sensing, or a related field, 12+ years of experience in remote sensing and satellite image analysis, Proven track record in building AI-based models for environmental change, Expert-level Python skills, Experience with the scientific stack (xarray, Dask, NumPy, Rasterio, GeoPandas), Experience with deep learning frameworks, Experience building automated pipelines for preprocessing and labeling planetary-scale datasets

Nice to Have

Specialized Environmental Research: Extensive experience specifically in flood damage quantification and methane-related water dynamics, Proven Funding & Publication Record: History of leading NASA-funded or similar high-impact geospatial research projects, Direct experience fine-tuning or modifying specific GFM architectures like TerraMind or Prithvi, A mix of deep academic rigor and the ability to prototype rapid-change monitoring tools for operational readiness

What You'll Do.

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

Systematically evaluate and compare existing GFMs against PlanetScope data to assess performance

Design embeddings and workflows optimized for detecting short-lived

high-impact events such as floods

rapid surface-water expansion

Explore the synergy between PlanetScope

and other commercial SAR data to ensure robust time-series analysis even under cloud cover

Use embeddings to design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks

Publish findings in top-tier journals and present at conferences

Oversee the technical direction of a dedicated postdoc and collaborate with Planet’s research scientists to transition prototypes into operational products

How You'll Work.

Team & Collaboration

Collaborate with a multi-disciplinary team of 'Planeteers' across space operations, data pipelines, and analytics; Collaborate with Planet’s research scientists

Communication Scope

Publish findings in top-tier journals; present at conferences

Process & Methodology

Oversee the technical direction of a dedicated postdoc

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