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Machine Learning Geospatial Jobs in Virginia (NOW HIRING)

GRVTY

Geospatial Analyst - Mid

Falls Church, VA · On-site

$85K - $105K/yr

... of Machine Learning algorithm training and evaluation. The ideal candidate brings strong multi-INT analytical skills and a passion for applying advanced geospatial tradecraft and data science ...

GRVTY

Geospatial Analyst - Mid

Falls Church, VA · On-site

$85K - $105K/yr

What Impact You'll Have GRVTY is seeking a motivated and experienced Geospatial Analyst to provide ... Experience working with or developing Machine Learning algorithms for exploiting overhead imagery ...

GRVTY

Geospatial Analyst - Mid

Falls Church, VA · On-site

$85K - $105K/yr

What Impact You'll Have GRVTY is seeking a motivated and experienced Geospatial Analyst to provide ... Experience working with or developing Machine Learning algorithms for exploiting overhead imagery ...

Planet

Senior Machine Learning Engineer

Arlington, VA · On-site +1

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

Planet

Senior Machine Learning Engineer

Arlington, VA · On-site

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

Planet Labs PBC

Senior Machine Learning Engineer

Arlington, VA · On-site

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

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All JobsMachine Learning Geospatial JobsMachine Learning Geospatial Jobs in Virginia

Machine Learning Geospatial information

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 the key skills and qualifications needed to thrive as a Machine Learning Geospatial professional?

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 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.

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Machine Learning Geospatial jobs near you

Internship - Machine Learning (Python) Research & Applied AI

Novateur Research Solutions

Ashburn, VA • On-site

Other

Re-posted 27 days ago

Job description

Internship Opportunity — Machine Learning (Python) | Research & Applied AI


Novateur Research Solutions, LLC is looking for motivated interns with strong Python programming skills to join our team working on cutting-edge machine learning and data-driven research.

We work on challenging, real-world problems where ML meets large-scale systems and complex data—ranging from healthcare to geospatial intelligence and simulation.

What you'll work on:

  • Medical image analysis (computer vision for healthcare applications)
  • Geospatial data mining and spatial intelligence
  • Long-term spatiotemporal modeling and forecasting
  • Large-scale microsimulation and computational modeling

Required Skills:

  • Strong Python programming ability
  • Experience with PyTorch or TensorFlow
  • Solid understanding of machine learning fundamentals
  • Comfort working with data structures and debugging code
  • Ability to learn quickly in a research-oriented setting

What you'll gain:

  • Hands-on experience solving real-world ML research problems
  • Exposure to interdisciplinary AI applications
  • Mentorship from experienced researchers and engineers
  • Opportunity to contribute to impactful, production-relevant research

Apply