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Geoai Jobs (NOW HIRING)

Overview Esri is enhancing the ArcGIS system with new AI capabilities that improve automation, expand GeoAI tools and models, strengthen productivity features, and deepen platform integrations across ...

Experience with ArcGIS, GeoAI, location-based services, geo-enabled apps, spatial analytics, or similar geospatial technology * Familiarity with Federal Sciences mission domains (such as health and ...

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Geoai information

What is GeoAI?

GeoAI, or Geographic Artificial Intelligence, refers to the integration of artificial intelligence (AI) techniques with geographic information systems (GIS) and spatial data. GeoAI leverages machine learning, deep learning, and other AI methods to analyze and interpret geospatial data, such as satellite imagery, maps, and sensor data. This technology is used in a variety of fields including urban planning, environmental monitoring, disaster response, and transportation. By automating the analysis of large and complex spatial datasets, GeoAI helps organizations make more informed decisions and discover patterns that would be difficult to detect manually.

What are the key skills and qualifications needed to thrive as a GeoAI Specialist, and why are they important?

To thrive as a GeoAI Specialist, you typically need a background in geospatial science, data analysis, and machine learning, often supported by a degree in GIS, computer science, or a related field. Familiarity with tools such as Python, ArcGIS, QGIS, and machine learning libraries like TensorFlow or PyTorch is essential, along with experience using spatial databases. Strong problem-solving skills, adaptability, and effective communication set standout professionals apart in this field. These abilities are crucial for leveraging AI to extract actionable insights from geospatial data, enabling data-driven decision-making and innovation.

How do GeoAI professionals typically collaborate with domain experts like urban planners or environmental scientists?

GeoAI professionals regularly work alongside domain experts such as urban planners, environmental scientists, and policy analysts to ensure that spatial data analyses and AI-driven insights are relevant and actionable. Collaboration often involves joint project meetings, data sharing, and iterative feedback loops to refine models and interpret results in the context of real-world applications. This interdisciplinary teamwork helps translate complex geospatial data into practical solutions for urban development, resource management, and disaster response. Effective communication and a willingness to learn from other fields are key to success in these collaborative environments.
More about Geoai jobs
What cities are hiring for Geoai jobs? Cities with the most Geoai job openings:
What states have the most Geoai jobs? States with the most job openings for Geoai jobs include:
Infographic showing various Geoai job openings in the United States as of June 2026, with employment types broken down into 80% Full Time, 10% Part Time, and 10% Contract. Highlights an 90% In-person, and 10% Remote job distribution.

Data Scientist (Colorado)

Kestrel Intelligence

Colorado Springs, CO

Full-time

Posted 28 days ago


Job description

The Mission
At Kestrel, we are building the future of scalable autonomy across domains. Our systems enable commercial and national security customers to sense and act in support of complex missions without significant autonomous system expertise. This technology bridges domains (e.g., space, air, land, sea) and fields (e.g., AI, orbital dynamics, geospatial), requiring a team that has both breadth and depth in these related topics.

The Role
We are looking for an individual contributor to architect and deploy production-grade subsystems that power our cutting-edge products for both commercial clients and critical national security missions. As a Kestrel Data Scientist, you will lead the development of high-fidelity data processing pipelines, traditional AI/ML models, and wrapping them into tools for next-generation Agentic AI. You will work at the bleeding edge of geospatial science. This role requires adaptability, engineering rigor, and a deep respect for the mission.

Core Responsibilities:
- Architecture: Lead the end-to-end creation of production-ready AI/ML and agentic subsystems.
- Geospatial Engineering: Build robust preprocessing pipelines for raster and vector data (using GDAL, SNAP, rasterio, PostGIS).
- Innovation: Integrate emerging technologies, from Geospatial Foundation Models (GeoFMs) to autonomous route planning, into a cohesive operational picture.

You Must Be:

A master of foundational geospatial: Proven experience building geospatial data preprocessing pipelines (raster and vector) using the modern geospatial stack (GDAL, SNAP, rasterio, PostGIS, etc.).

A specialist: You must have production-grade experience in at least two (2) of the following domains:
- Traditional GeoAI/ML: Object detection/land classification (scikit, OpenCV, etc.).
- Geospatial Foundation Models: Utilizing GeoFMs like AlphaEarth, Prithvi, or Clay.
- SAR & Interferometry: Pipeline development (e.g., isce2).
- Hyperspectral Imaging: Processing (e.g., SPy).
- RF Propagation: Modeling signal data (e.g., CloudRF).
- Digital Elevation Models: Handling DEM in multiple formats (e.g., DTED).
- Autonomous Systems: Route planning for unmanned systems (e.g., Auterion, ArduPilot).
- Optimization: Complex scheduling for satellite constellations or aircraft.
- Economics: Models for resource allocation such as auction, bidding, pricing, waiting.

Must be a US Citizen

Bonus:
- Based in the Colorado Springs, CO or Washington, DC (working in customer sites in Crystal City, VA and Herndon, VA) areas
- Active Top Secret // Specially Compartmented Information (TS//SCI) clearance with counterintelligence-scope polygraph (CSP) or full-scope polygraph (FSP)
 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.