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Internship Google Earth Engine Jobs in Springfield, VA

GRVTY

Imagery Scientist (EO) - Expert

Falls Church, VA · On-site

Google Earth Engine * Deep understanding of: * Remote sensing principles * Advanced imagery processing and exploitation methods * Sensor and platform limitations and advantages * Proven ability to ...

GRVTY

Imagery Scientist (EO) - Expert

Falls Church, VA · On-site

Python, MATLAB, Google Earth Engine • Deep understanding of: Remote sensing principles, Advanced imagery processing and exploitation methods, Sensor and platform limitations and advantages • ...

Booz Allen Hamilton

Expert EO Imagery Scientist

Alexandria, VA · Hybrid

$99K - $225K/yr

  • Medical

  • Life

  • Retirement

  • PTO

... Google Earth Engine, to automate scientific processes of EO imagery data to aid analysis * Experience communicating with a variety of technical and non-technicalaudiences on the availability and ...

Leidos

Imagery Scientist (SAR)

Springfield, VA · On-site

$116K - $210K/yr

  • Medical

  • Retirement

  • PTO

Proficiency in Google Earth Engine, ESRI ARC Services, Tableau, Web Services Development and ArcGIS. #GEOINTMISSION #NSBA If you're looking for comfort, keep scrolling. At Leidos, we outthink ...

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How much do internship google earth engine jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for internship google earth engine in Springfield, VA is $17.39, according to ZipRecruiter salary data. Most workers in this role earn between $15.05 and $19.33 per hour, depending on experience, location, and employer.

What is an internship in Google Earth Engine?

An Internship in Google Earth Engine is a temporary position where students or recent graduates work with Google’s cloud-based geospatial processing platform. Interns typically assist with analyzing satellite imagery, developing geospatial applications, and supporting environmental or research projects. This role helps interns gain valuable experience in remote sensing, programming, and data analysis, while contributing to real-world solutions using Google Earth Engine. Interns often collaborate with experienced engineers, researchers, and other professionals, making it a great learning opportunity.

What types of projects do interns typically work on during a Google Earth Engine internship?

As a Google Earth Engine intern, you can expect to work on projects involving large-scale geospatial data analysis, development of tools or scripts for environmental monitoring, or collaborating with external partners on research initiatives. Interns often contribute to real-world solutions, such as mapping deforestation, monitoring urban growth, or developing visualizations for climate data. You'll typically work closely with experienced engineers, data scientists, and researchers, gaining exposure to both the technical and collaborative aspects of the platform. This hands-on experience helps build valuable skills in cloud computing, remote sensing, and data visualization.

What are the key skills and qualifications needed to thrive as an internship in Google Earth Engine?

To succeed in a Google Earth Engine internship, you need a solid background in remote sensing, geospatial analysis, and programming languages like Python or JavaScript, often supported by ongoing or completed studies in geography, environmental science, or computer science. Familiarity with the Google Earth Engine platform, GIS software (such as QGIS or ArcGIS), and data visualization tools is typically required. Strong analytical thinking, problem-solving abilities, and effective communication help you interpret results and collaborate within diverse project teams. These skills are crucial for efficiently processing geospatial data, deriving valuable insights, and contributing to impactful environmental or societal projects.

What is the difference between Internship Google Earth Engine vs Data Analyst Intern?

AspectInternship Google Earth EngineData Analyst Intern
Required SkillsBasic programming, GIS, remote sensing, Google Earth Engine platform knowledgeData analysis, Excel, SQL, visualization tools
Work EnvironmentEnvironmental, geospatial, remote sensing projectsBusiness, finance, marketing, or research sectors
Industry UsageGeospatial analysis, environmental monitoring, climate studiesData interpretation, reporting, decision support

Internship Google Earth Engine focuses on geospatial data analysis using Google Earth Engine, ideal for environmental and remote sensing projects. In contrast, Data Analyst Interns work with broader data sets across industries, emphasizing data interpretation and visualization. Both roles require analytical skills but differ in technical tools and industry focus.

What job categories do people searching Internship Google Earth Engine jobs in Springfield, VA look for?

The top searched job categories for Internship Google Earth Engine jobs in Springfield, VA are:

What cities near Springfield, VA are hiring for Internship Google Earth Engine jobs?

Cities near Springfield, VA with the most Internship Google Earth Engine job openings:

Internship Google Earth Engine jobs near you

Imagery Scientist (EO) - Expert

GRVTY

Falls Church, VA • On-site

Full-time

Re-posted yesterday

Job description

What Impact You'll Have

GRVTY is seeking a motivated and experienced Imagery Scientist to serve as the subject matter expert on EO imagery and provide technical direction across the full imagery acquisition and preparation lifecycle. The ideal candidate is an experienced EO imagery scientist with a passion for advanced scientific problem solving, imagery exploitation, and operational intelligence support. This role will drive solutions informed by specific phenomenology limitations and advantages of sensors and platforms.

What You'll be Owning

  • Integrate emerging sensors and platforms into existing data pipelines and workflows, including:
    • Developing pipelines and relevant data structures for emerging capabilities
    • Incorporating new data types into existing data workflows
  • Conduct assessment of potential differences between new sensor characteristics and capabilities compared to currently utilized platforms
  • Assess potential differences in metadata, data format, and data structure characteristics in regard to changes to:
    • Databases and schemas
    • APIs and other ETL-related processes
    • Ingestion and movement of data within the existing data operations pipeline
  • Determine how to acquire new data, including:
    • Potential latency associated with acquisition
    • Data formats and security domains
  • Determine how to pre-process and standardize data to match existing standards or be transformed into a usable state for labeling and model development purposes, including:
    • Converting between file format types
    • Tiling full-size images into specified sizes or geospatial bounds
  • Investigate gaps in emerging sensor capabilities that may require supplementation from other sources
  • Collaborate with SAR Imagery Scientists to provide coincident EO imagery aligned with geographic areas and temporal windows of emerging capabilities
  • Develop and implement mathematical conversion models to:
    • Transform data labels across multiple imagery types
    • Convert between orthorectified and non-orthorectified imagery
  • Develop methods to execute tiling and pre-processing of full-size raw imagery into specified sizes and data formats while maintaining metadata and data integrity
  • Generate tiles from full-size image collects with specified pixel dimensions or geospatial boundaries, accounting for orthorectification to ensure geospatial accuracy at tile edges
    • Analyze and assess image quality and sensor metadata to:
  • Execute day-to-day management of all imagery curation, acquisition, and pre-processing activities, including:

What You Must Have

  • Active TS/SCI Clearance with the ability to obtain a CI/Poly
  • Exhibit understanding of:
    • Collection and phenomenology
    • Image formation processes
    • Exploitation products and methodologies
  • Experience exploiting the electromagnetic spectrum (e.g., electro-optical) to determine the occurrence and location of objects of interest
  • Experience developing, testing, and evaluating:
    • New algorithms, processes, methodologies, and exploitation products
    • Automated scientific workflows using advanced processing tools including:
      • Python
      • MATLAB
      • Google Earth Engine
  • Deep understanding of:
    • Remote sensing principles
    • Advanced imagery processing and exploitation methods
    • Sensor and platform limitations and advantages
  • Proven ability to communicate technical concepts to:
    • Technical audiences
    • Non-technical stakeholders
    • Intelligence users

What Would be Nice to Have

  • Experience applying CV and machine learning (ML) techniques to EO imagery and data to address intelligence problems

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