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Machine Learning Geospatial Jobs in San Francisco, CA

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

$110 - $170/hr

Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation systems, environmental ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation systems, environmental ...

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation systems, environmental ...

Pytho AI, Inc

AI Engineer

San Francisco, CA · On-site

Solid understanding of machine learning principles (model evaluation, optimization, bias/variance ... Exposure to geospatial, multimodal, or reasoning systems * Experience with containerization and ...

Nash, Inc.

Senior Data Scientist

San Francisco, CA · On-site +1

Work with large, messy operational datasets, including delivery events, geospatial data, carrier ... What you'll bring * 4+ years of experience as a Data Scientist, Machine Learning Engineer, or in a ...

Nash

Senior Data Scientist

San Francisco, CA · On-site

$180 - $240/hr

Work with large, messy operational datasets, including delivery events, geospatial data, carrier ... What you'll bring * 4+ years of experience as a Data Scientist, Machine Learning Engineer, or in a ...

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All JobsMachine Learning Geospatial JobsMachine Learning Geospatial Jobs in San Francisco, CA

Machine Learning Geospatial information

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$22

$34

$54

How much do machine learning geospatial jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for machine learning geospatial in San Francisco, CA is $34.34, according to ZipRecruiter salary data. Most workers in this role earn between $26.63 and $39.95 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 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 San Francisco, CA?

For Machine Learning Geospatial jobs in San Francisco, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Geospatial jobs in San Francisco, CA look for?

The top searched job categories for Machine Learning Geospatial jobs in San Francisco, CA are:

What cities near San Francisco, CA are hiring for Machine Learning Geospatial jobs?

Cities near San Francisco, CA with the most Machine Learning Geospatial job openings:

Machine Learning Geospatial jobs near you
Infographic showing various Machine Learning Geospatial job openings in San Francisco, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 81% In-person, 6% Hybrid, and 13% Remote job distribution, with an average salary of $71,429 per year, or $34.3 per hour.

Data Annotator / Geospatial Annotation Specialist

South San Francisco, CA

Aechelon Technology
Software Development • 1 - 5K employees
Learn more about Aechelon Technologyexternal

$82K - $92K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 10 days ago

Job description

The Data Annotator / Geospatial Annotation Specialist plays a critical role in the creation of high-quality training datasets used to develop and refine Aechelon's machine learning and computer vision models. This role supports both the Advanced Model Development Group and the Applied Real-Time Vision Group, ensuring datasets for object detection, segmentation, and classification are accurate, consistent, and production-ready.
The Specialist performs detailed vector annotation, image segmentation, and dataset preparation while adhering to strict quality standards. Because model performance is highly dependent on high-quality annotation, this role requires exceptional attention to detail and a strong understanding of geospatial imagery.
In addition to dataset creation, the Specialist will learn core machine learning concepts and gain experience operating inference tools and models within the DAML pipeline, becoming a direct contributor to model evaluation and workflow improvements.

Key Responsibilities
  • Create precise vector annotations and segmentation masks for training computer vision and object detection models.
  • Perform detailed image segmentation, manually labeling features across large and varied imagery datasets.
  • Follow established annotation guidelines and maintain consistency across global AOIs.
  • Validate and refine automated detection outputs; correct errors or incomplete detections.
  • Work with ML team to understand annotation needs, edge cases, and quality thresholds.
  • Learn how to operate model inference tools and assist in evaluating model performance.
  • Provide feedback on false positives/negatives, detection weaknesses, and annotation ambiguities.
  • Maintain structured documentation of annotation processes, datasets, feature definitions, and QA results.
  • Support improvements to dataset pipelines and annotation workflows through iterative refinement and testing.
  • Assist multiple DAML groups as needed, depending on dataset demands and model development cycles.
Required Qualifications
  • Background in GIS, Remote Sensing, Image Analysis, Digital Art, Photography, or related field (degree preferred but not required with strong experience).
  • Prior experience with image annotation, data labeling, GIS feature extraction, or segmentation workflows.
  • Ability to visually identify subtle features in imagery with extreme precision.
  • Strong analytical, organizational, and documentation skills.
  • Ability to work with large datasets for extended periods while maintaining accuracy and focus.
Required Skills and Tools
  • Adobe Photoshop (Advanced): Expertise in mask creation, polygon tracing, color differentiation, clean-up workflows, and segmentation editing.
  • GIS Tools (Intermediate+): Ability to work in QGIS, ERDAS Imagine, or Global Mapper for spatial visualization and annotation support.
  • Geospatial Data Handling: Ability to work with shapefiles, GeoPackages, raster datasets, and other formats used in ML workflows.
  • Python (Basic-Intermediate): Ability to run scripts, perform data checks, and assist with pre-processing tasks.
  • Documentation Tools: Proficiency using Jupyter Notebook and Git for tracking annotation notes and revisions.
Strongly Desired Skills and Tools
  • Experience creating training datasets for machine learning, object detection, or image segmentation models.
  • Familiarity with YOLO, PyTorch, or fast.ai (conceptual knowledge acceptable).
  • Ability to create simple scripts to automate annotation steps or pre-processing tasks.
  • Experience using ChatGPT or other LLMs to improve workflows, generate helper scripts, or automate documentation.
  • Understanding of geospatial features such as vegetation, buildings, vehicles, aircraft, or other runtime elements.
Reporting Expectations

The Specialist reports jointly to managers in the Advanced Model Development and Applied Real-Time Vision groups depending on project assignment. Regular updates are expected on dataset progress, annotation quality, workflow blockers, and model evaluation findings. The Specialist is expected to meet annotation quotas while maintaining strict accuracy and quality standards.

Compensation

$82,000 - 92,000 / year 

The above range is specific to CALIFORNIA and may not be applicable to other locations. Final compensation is based on factors such as the candidate's skills, qualifications, and experience. 

 We offer a very attractive compensation package including competitive base salary, company performance-based profit sharing, 401k, 100% employer paid health benefits (medical, dental, vision, life, std, ltd, and life insurance plans). 

No relocation reimbursement provided. 

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