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Machine Learning Geospatial Jobs (NOW HIRING)

Overstory

Role & Team As a Staff Machine Learning Engineer at Overstory, you will lead the development and ... Design and maintain robust data and feature pipelines for large-scale geospatial and temporal data.

Oxman

Machine Learning Research Engineer

New York, NY · On-site

$142K - $224K/yr

Develop machine learning models for geospatial inference of key ecosystem metrics, leveraging geospatial AI to synthesize environmental data into actionable parameters for ecosystem design and ...

asrcfh

Geospatial Analyst

Reston, VA · On-site +1

Use machine learning approaches for remote sensing and agricultural analytics * Produce maps ... Stay current on geospatial technologies, industry trends, and analytical methods Basic ...

asrcfh

Geospatial Analyst

Reston, VA · On-site +1

Use machine learning approaches for remote sensing and agricultural analytics * Produce maps ... Stay current on geospatial technologies, industry trends, and analytical methods Basic ...

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How much do machine learning geospatial jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for machine learning geospatial in the United States is $29.15, according to ZipRecruiter salary data. Most workers in this role earn between $22.60 and $33.89 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.
More about Machine Learning Geospatial jobs
What cities are hiring for Machine Learning Geospatial jobs? Cities with the most Machine Learning Geospatial job openings:
What states have the most Machine Learning Geospatial jobs? States with the most job openings for Machine Learning Geospatial jobs include:
What job categories do people searching Machine Learning Geospatial jobs look for? The top searched job categories for Machine Learning Geospatial jobs are:
Machine Learning Geospatial jobs near you
Infographic showing various Machine Learning Geospatial job openings in the United States as of May 2026, with employment types broken down into 67% Full Time, and 33% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $60,627 per year, or $29.1 per hour.

Machine Learning SWE Lead - Geospatial, Bellwether

X, the moonshot factory

Mountain View, CA • On-site

Full-time

Posted 25 days ago

Job description

M a c h i n e L e a r n i n g S W E L e a d - G e o s p a t i a l , B e l l w e t h e r
Software Engineering Mountain View, CA (HQ)
About X, the Moonshot Factory
X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world's most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.
About the Team:
Our team - Bellwether - operates at the intersection of machine learning, geospatial data, and pressing issues with enterprise customers, governments, and more. We focus on severe weather manifestations of climate change, such as wildfires, and aim to use a wide range of data and analytics to better understand and predict what these events could mean for communities and businesses across the globe. We do this through the detection and classification of the features and patterns of the natural and built worlds, the application of cutting-edge machine learning, and the redesign of the full geo-ML workflow. This work extends into development and production-level work as well, as Bellwether also has a number of products that are live with customers. The result: tools and models that allow a range of industries to better leverage earth observation insights.
About the role:
You will be a hands-on Machine Learning engineer, contributing to all aspects of the project's development and deployment of applications. We are looking for a motivated expert level ML Engineer with broad experience across systems architecture and design in one or more cloud platforms. This role would guide machine learning for Bellwether's real-world products, and is not a research-based role.
Our team is small but mighty and highly collaborative, and values pair programming and cooperative ideation. We are committed to agile principles and rely heavily on this framework for efficient sprints and cycles. We are looking for passionate and driven people, who are comfortable moving between creative, big-picture thinking and specifics of how to execute. We operate in a fast-paced, fluid environment as our team moves from early stage development into production phases.
How you will make 10x impact:
  • Embracing your ability to be a ML 'expert generalist', enjoying the fluidity of moving from architecting new production systems, to machine learning, to security and monitoring. From high level strategy to specific tactics.
  • Help drive and execute key decisions on software architecture and features, balancing business needs and our technology roadmap, balancing longevity with rapid prototyping
  • Contribute as a key team member to the creation of new systems and processes to ensure high quality development, deployment, and maintenance of live applications in production environments
  • Create and maintain Google Cloud Platform-based infrastructure for software development and high-volume production systems.
  • Present findings to team members, internal and external stakeholders, and help set a direction for future development.

What you should have:
  • Master's degree or PhD in Computer Science, Applied Mathematics, Physics, or equivalent practical experience
  • 7 years experience with the machine learning development pipeline: research, experimentation, and ML-Ops
  • Experience in engineering management or as a technical lead, with a track record of guiding team strategy and mentoring engineers while remaining hands-on.
  • Expertise in ML frameworks (e.g., PyTorch, TensorFlow/Keras/JAX) and Python libraries (e.g., NumPy, SciPy, Pandas).
  • Experience with numerous common software design patterns (for example, Observer, Decorator, Visitor, Producer/Consumer, etc).
  • Experience with open source tools such as: Git, TensorFlow, Apache Beam/Dataflow, Google Compute Engine.
  • Python proficiency.
  • Experience working on an early stage project and environment where prototype technologies are evolved into a production phase.
  • An ability to thrive in an Agile-driven team: iteratively sprinting toward goals and products, contributing new ideas, standards, and processes.
  • Experience interfacing with customers

It'd be great if you also had these:
  • Production-level experience in the geospatial industry, with a wide variety of tasks, including code development, designing for, implementing, and managing security measures and controls, troubleshooting and debugging, designing and implementing code testing processes, and monitoring deployed application's performance and health.
  • Experience working with a wide variety of geospatial data
  • Experience in Machine Learning Operations - scaling existing machine learning applications into production

The US base salary range for this full-time position is $197,000 - $311,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
An Equal Opportunity Workplace
At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you have a disability or special need that requires accommodation, please contact us at x-accommodation-request@x.team .

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