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Machine Learning Geospatial Jobs in Missouri (NOW HIRING)

Jobgether

$80K - $110K/yr

Our partner is looking for a Senior Geospatial Machine Learning Engineer based in Netherlands. Join a fully remote, mission-driven climate technology environment where machine learning and satellite ...

Esri

Implement best practices and patterns for geospatial machine learning and develop reusable technical components for demonstrations and rapid prototyping * Keep up to date with the latest technology ...

CACI

Senior Data Scientist

Saint Louis, MO · On-site

$82K - $172K/yr

Apply advanced analytics and machine learning to mission problems for the National Geospatial-Intelligence Agency (NGA). Work at the intersection of data science and geospatial intelligence, turning ...

Posted today

Jobgether

Opportunity to work on advanced machine learning, computer vision, geospatial analytics, and AI challenges. * Exposure to large-scale aerial and satellite imagery and technology supporting property ...

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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.

What are popular job titles related to Machine Learning Geospatial jobs in Missouri?

For Machine Learning Geospatial jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Machine Learning Geospatial jobs in Missouri look for?

The top searched job categories for Machine Learning Geospatial jobs in Missouri are:

What cities in Missouri are hiring for Machine Learning Geospatial jobs?

Cities in Missouri with the most Machine Learning Geospatial job openings:

Machine Learning Geospatial jobs near you

Senior Geospatial Machine Learning Engineer

Jobgether

On-site, Remote

$80K - $110K/yr

Full-time

Posted 22 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Geospatial Machine Learning Engineer based in Netherlands.

Join a fully remote, mission-driven climate technology environment where machine learning and satellite imagery are used to address critical infrastructure challenges.
As part of the Vegetation Modeling team, you will build advanced ML solutions that identify vegetation-related risks before they contribute to wildfires or power outages.
You will work with large-scale geospatial datasets, satellite and aerial imagery, computer vision, and deep learning to create production-ready intelligence products.
The role combines hands-on engineering with technical ownership, giving you the opportunity to lead projects from initial planning through delivery.
You will collaborate with teams across Europe and the Americas, influencing data pipelines, platform architecture, model evaluation, and product delivery.
Your work will directly contribute to improving grid resilience while applying technology to complex environmental and climate challenges.
This is an opportunity for a senior ML professional who wants meaningful technical challenges and measurable real-world impact.

Accountabilities
  • Develop and deploy new vegetation intelligence products using machine learning, deep learning, computer vision, geospatial Python libraries, and large-scale satellite or aerial imagery.
  • Explore geospatial datasets, identify opportunities for model improvement, optimize existing ML solutions, and troubleshoot production issues.
  • Maintain and enhance existing vegetation modeling products to improve accuracy, reliability, scalability, and overall impact.
  • Lead technical projects end-to-end, from defining objectives and planning implementation through execution, delivery, and evaluation.
  • Develop measurement frameworks, evaluation tooling, and performance metrics that enable data-driven decisions about model quality and impact.
  • Monitor production models and investigate performance issues using appropriate observability, monitoring, and debugging tools.
  • Work closely with upstream data ingestion teams to influence data pipelines, processing workflows, and platform architecture.
  • Partner with downstream product and delivery teams to ensure geospatial ML outputs can be effectively integrated into customer-facing solutions.
  • Use tools such as QGIS, Dagster, Sentry, Grafana, or equivalent platforms to analyze data, manage workflows, monitor systems, and diagnose issues.
  • Communicate technical findings, project progress, model performance, and business impact clearly to technical and non-technical stakeholders.
  • Contribute to engineering and ML best practices across a distributed team working across Europe and the Americas.
  • Help translate complex environmental and geospatial problems into scalable machine learning solutions that support climate resilience and critical infrastructure.
Requirements:
  • 5+ years of professional experience as a Machine Learning Engineer, Data Scientist, or in a closely related role, with demonstrated experience building and deploying production machine learning or deep learning models.
  • Proven experience developing computer vision or deep learning models using satellite or aerial imagery.
  • Strong proficiency in Python and geospatial Python libraries such as rasterio, geopandas, shapely, GDAL, or equivalent technologies.
  • Solid understanding of geospatial data structures, formats, processing workflows, and analysis techniques.
  • Professional experience with ML and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, or comparable tools.
  • Experience designing, implementing, or maintaining data pipelines using orchestration and workflow tools such as Dagster, Airflow, dbt, or equivalent systems.
  • Experience with QGIS or comparable geospatial visualization and analysis software.
  • Strong understanding of model evaluation, performance measurement, monitoring, and debugging in production environments.
  • Ability to work effectively with large-scale, complex datasets and translate technical findings into practical product or business decisions.
  • Strong project ownership skills, with the ability to independently drive initiatives from planning through execution and delivery.
  • Excellent communication and collaboration skills, particularly in distributed and cross-functional environments.
  • Experience with multispectral or hyperspectral satellite imagery is a strong advantage.
  • Background in vegetation analysis, forestry, agriculture, environmental monitoring, or related geospatial applications is highly valued.
  • Familiarity with observability and monitoring tools such as Grafana, Sentry, Prometheus, or similar platforms is a plus.
  • A genuine interest in climate technology, environmental applications, and using advanced technology to solve complex real-world problems is highly desirable.
  • Candidates should be comfortable working in a fully remote environment and collaborating across multiple time zones.
Benefits:
  • Fully remote working environment.
  • Opportunity to work on technology with direct applications in climate action, wildfire prevention, and electrical grid resilience.
  • Meaningful ownership of machine learning products and the opportunity to lead projects from concept through production.
  • Collaboration with a geographically distributed team spanning Europe and the Americas.
  • Exposure to advanced satellite imagery, geospatial data, computer vision, and large-scale machine learning systems.
  • High-impact technical challenges involving real-world environmental and infrastructure problems.
  • Competitive senior-level compensation package expected, commensurate with experience.
  • Opportunity to contribute to the development of production ML systems rather than purely experimental or research-focused models.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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