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Machine Learning Geospatial Jobs in Lexington, MA

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Applied ML Engineer

Boston, MA · Hybrid

$190K - $250K/yr

Stay current with the latest research and tools in machine learning and evaluate their ... Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.

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Applied ML Engineer

Boston, MA · On-site +1

Stay current with the latest research and tools in machine learning and evaluate their ... Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.

Verisk

ML Scientist (Atmospheric Science)

Boston, MA · On-site

$115K - $135K/yr

... geospatial data processing and visualization tools (e.g., xarray, NetCDF, pandas). • High degree of comfort deploying machine learning models in HPC environments. • Excellent verbal and written ...

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How much do machine learning geospatial jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for machine learning geospatial in Lexington, MA is $32.75, according to ZipRecruiter salary data. Most workers in this role earn between $25.38 and $38.08 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 job categories do people searching Machine Learning Geospatial jobs in Lexington, MA look for? The top searched job categories for Machine Learning Geospatial jobs in Lexington, MA are:
Machine Learning Geospatial jobs near you
Infographic showing various Machine Learning Geospatial job openings in Lexington, MA as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $68,117 per year, or $32.7 per hour.
Lead Research Scientist - Machine Learning

Lead Research Scientist - Machine Learning

STR

Woburn, MA • On-site

$174K - $220K/yr

Other

Posted yesterday

Job description

About the Team

STR's Analytics and C2 Division researches and develops novel technologies to solve challenging national security problems through advanced analytics. Our team consists of passionate and motivated engineers and scientists with advanced degrees in engineering, computer science, mathematics, physics, and data science. We use our expertise and creativity to take innovative ideas from conception to mature implementation to improve mission success of our customers.

The Signals Exploitation and Tracking (SET) Group in the Analytics and C2 Division focuses on applying machine learning, statistics, estimation theory, and information theory algorithms for signals exploitation, target tracking, predictive analytics, and system resource management.

The Role

As a Lead Research Scientist at STR, you will help develop disruptive technologies focused on signals exploitation, estimation theory, system resource management, and systems analysis.  You will lead the development of cutting-edge AI/ML algorithms for novel application domains and modalities, participate on and lead project teams, and interact with customers. You will explore fascinating datasets, develop cutting-edge algorithmic techniques, and solve high-impact, unique problems for our customers. We are looking for someone to join our team onsite as this position is based primarily in the Woburn, MA office.

Who you are:

  • MS with at least 8 years of experience, and/or PhD with at least 5 years of experience (or equivalent experience) in a scientific field such as applied math, physics, electrical engineering, computer science, or data science
  • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including implementing new layers/network architectures, model training, hyperparameter tuning, and ablation studies
  • Experience adapting novel machine learning approaches (e.g., from academic literature) to new data sets and problems
  • Experience with standard data science tools such as scikit-learn, Pandas, and Matplotlib
  • Proficiency in one or more programming languages: Python, C/C++
  • Able to work, collaborate on, and lead multi-disciplinary teams
  • Able to communicate technical foundations of models and algorithms to technical and non-technical audiences
  • Ability to obtain and maintain a security clearance, for which U.S citizenship is needed by the U.S government

Even better:

  • Active US government security clearance
  • Experience applying deep learning to domains other than images/text, such as time series, discrete event sequence, or geospatial
  • Experience with self-supervised machine learning
  • Expertise working with time series, geospatial, and/or spatio-temporal data
  • Experience in intelligence or military-related mission areas

Pay Information
Full-Time Salary Range: $174,000 - $220,000

The salary range listed is based on external market data. Offers are based on factors, such as but not limited to, the candidate's experience, education, training, key skills/critical skills, security clearances, and prevailing market and business conditions.

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