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Machine Learning Geospatial Jobs in Ashburn, VA (NOW HIRING)

Quevera LLC

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

... machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and develop data processing and evaluation frameworks for large-scale geospatial imagery ...

Quevera LLC

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

... machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and develop data processing and evaluation frameworks for large-scale geospatial imagery ...

UNISSANT

Data Scientist

Washington, DC · On-site

Experience building dashboards, visualizations, and geospatial analytical products using tools such as Tableau, Qlik, Power BI, ArcGIS, matplotlib, or seaborn. * Familiarity with machine learning and ...

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All JobsMachine Learning Geospatial JobsMachine Learning Geospatial Jobs in Ashburn, VA

Machine Learning Geospatial information

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How much do machine learning geospatial jobs pay per hour?

As of Jul 26, 2026, the average hourly pay for machine learning geospatial in Ashburn, VA is $29.81, according to ZipRecruiter salary data. Most workers in this role earn between $23.12 and $34.66 per hour, depending on experience, location, and employer.

What does a Machine Learning Geospatial professional do?

A Machine Learning Geospatial professional uses machine learning techniques to analyze and interpret geospatial data, such as satellite imagery, maps, and GPS data. Their work involves building and training models to detect patterns, make predictions, and solve spatial problems in fields like agriculture, urban planning, disaster response, and environmental monitoring. These professionals often collaborate with data scientists and GIS (Geographic Information Systems) specialists to extract actionable insights from large and complex geospatial datasets. Their skills are crucial for automating tasks such as image classification, land cover mapping, and object detection in geographic contexts.

What are some common challenges faced by Machine Learning Geospatial professionals when integrating spatial data into predictive models?

Machine Learning Geospatial professionals often encounter challenges such as managing large and complex spatial datasets, ensuring data quality and consistency, and handling spatial autocorrelation that can bias model results. Additionally, integrating diverse data sources—like satellite imagery, sensor data, and GIS layers—requires advanced pre-processing and domain knowledge. Collaborating with GIS analysts and domain experts is usually essential to develop robust models that provide actionable insights.

What is the difference between Machine Learning Geospatial vs GIS Analyst?

AspectMachine Learning GeospatialGIS Analyst
Required CredentialsBachelor's or higher in Computer Science, Data Science, or related fields; knowledge of machine learning and geospatial dataBachelor's in Geography, GIS, or related fields; proficiency in GIS software
Work EnvironmentTech companies, data science teams, research institutionsGovernment agencies, urban planning, environmental firms
Industry UsageData-driven geospatial analysis, predictive modeling, AI applicationsMapping, spatial data management, spatial analysis

Machine Learning Geospatial professionals focus on applying machine learning techniques to analyze geospatial data, often working with large datasets and developing predictive models. GIS Analysts primarily handle spatial data management, mapping, and analysis using GIS software. While both roles work with geospatial data, Machine Learning Geospatial roles emphasize data science and AI, whereas GIS Analysts focus on spatial information management and visualization.

What are the key skills and qualifications needed to thrive as a Machine Learning Geospatial specialist, and why are they important?

To thrive as a Machine Learning Geospatial specialist, you need a strong background in machine learning, geospatial analysis, programming (Python, R), and a relevant degree in computer science, geography, or a related field. Familiarity with GIS software (e.g., ArcGIS, QGIS), remote sensing tools, and cloud platforms like Google Earth Engine or AWS is typically required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with multidisciplinary teams. These skills and qualities are crucial for developing accurate geospatial models and delivering actionable insights from complex spatial data.
What are popular job titles related to Machine Learning Geospatial jobs in Ashburn, VA? For Machine Learning Geospatial jobs in Ashburn, VA, the most frequently searched job titles are:
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What cities near Ashburn, VA are hiring for Machine Learning Geospatial jobs? Cities near Ashburn, VA with the most Machine Learning Geospatial job openings:
Machine Learning Geospatial jobs near you
Electro-Optical Data Scientist/Imagery Scientist with Security Clearance

Electro-Optical Data Scientist/Imagery Scientist with Security Clearance

NorthHill Technology Resources

Falls Church, VA • On-site

Other

Posted 9 days ago

Job description

We are seeking an experienced Electro-Optical (EO) Data Scientist Specialist / Imagery Scientist to join a high-performing team supporting advanced AI and machine learning initiatives for complex national security and intelligence missions. This program focuses on evaluating AI models against Government datasets to assess their performance, resilience, and robustness across a broad range of adversarial scenarios. The effort also includes testing and validating autonomous algorithms designed to support operational decision-making in dynamic mission environments. As an EO Exploitation Specialist / Imagery Scientist, you will leverage your expertise in Electro-Optical imagery, geospatial analysis, and multi-INT exploitation to support the curation, analysis, and preparation of imagery datasets used for machine learning model development, testing, and evaluation. You will work alongside engineers, data scientists, and mission analysts to ensure imagery products are representative, mission-relevant, and optimized for AI/ML applications. Required Qualifications
Active TS/SCI clearance with eligibility for CI Polygraph (we can sponsor your CI poly if you don't already have one)
Experience exploiting Electro-Optical (EO) imagery and EO analytical products in support of intelligence or geospatial missions.
Experience conducting multi-INT analysis and integrating data from multiple intelligence disciplines to support mission objectives.
Proficiency working with structured, semi-structured, and unstructured datasets using programming, scripting, and visualization tools.
Strong geospatial analysis skills and the ability to apply emerging technologies, analytical methodologies, and data-driven techniques to solve complex intelligence challenges.
Preferred Qualifications
Experience developing or applying machine learning (ML) and computer vision (CV) techniques to overhead imagery, motion imagery, or other geospatial datasets.
Familiarity with AI/ML workflows supporting imagery exploitation, object detection, and model evaluation.
Key Responsibilities
Leverage multi-INT data, with an emphasis on Electro-Optical (EO) imagery, to identify, curate, and prepare imagery collected from emerging sensor platforms for AI/ML model development, testing, evaluation, and accreditation.
Curate imagery that aligns with Government priorities by selecting datasets containing required objects of interest, geographic coverage, scene characteristics, and other operationally relevant attributes.
Evaluate the capabilities and limitations of emerging EO sensors and recommend data curation strategies that maximize dataset quality, diversity, and operational value.
Identify data gaps and recommend additional collections or complementary data sources to develop representative datasets that support robust machine learning model evaluation.
Collaborate with cross-functional teams to ensure curated imagery meets quality standards and is optimized for advanced analytics, AI/ML development, and mission execution.

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