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Machine Learning Geospatial Jobs in Cupertino, CA

Anori

Senior Backend Engineer

San Mateo, CA · On-site

Build data orchestration pipelines to manipulate geospatial, vector and text-based data * Collaborate with AI engineers to build scalable, reproducible deployment pipelines for machine learning ...

Shakudo

Forward Deployed Engineer

Menlo Park, CA

Ability to apply AI to real-world operational data, such as sensor data, geospatial data, logistics ... Experience with data engineering, machine learning, or data science workflows, including feature ...

Tailored Brands

Sr Store Analytics Manager

Dublin, CA · On-site +1

... machine learning, and external market data to drive profitable growth. What You'll Bring | Skills ... geospatial analysis, or related retail growth disciplines. • Experience working with external ...

Tailored Brands

Sr Store Analytics Manager

Dublin, CA · On-site

... machine learning, and external market data to drive profitable growth. What You'll Bring | Skills ... geospatial analysis, or related retail growth disciplines. • \tExperience working with external ...

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All JobsMachine Learning Geospatial JobsMachine Learning Geospatial Jobs in Cupertino, CA

Machine Learning Geospatial information

See Cupertino, CA salary details

$23

$35

$57

How much do machine learning geospatial jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for machine learning geospatial in Cupertino, CA is $35.96, according to ZipRecruiter salary data. Most workers in this role earn between $27.88 and $41.83 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 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 job categories do people searching Machine Learning Geospatial jobs in Cupertino, CA look for? The top searched job categories for Machine Learning Geospatial jobs in Cupertino, CA are:
What cities near Cupertino, CA are hiring for Machine Learning Geospatial jobs? Cities near Cupertino, CA with the most Machine Learning Geospatial job openings:
Machine Learning Geospatial jobs near you
Infographic showing various Machine Learning Geospatial job openings in Cupertino, CA as of August 2026, with employment types broken down into 77% Full Time, and 23% Contract. Highlights an 60% In-person, and 40% Remote job distribution, with an average salary of $74,798 per year, or $36 per hour.

Senior Backend Engineer

Anori

San Mateo, CA • On-site

Full-time

Re-posted 10 days ago

Job description

About the Team:
We are a team of engineers, scientists, and domain experts dedicated to making housing and building development more sustainable and equitable. At Anori, we believe that to meet the global housing demand, we must build differently than we have before.
We are developing novel AI solutions to address complex challenges in these industries, driving a transformation in building performance, efficiency, and sustainability. This is a rare opportunity to shape an early-stage company in an extremely meaningful way.
The Role:
We are looking for versatile backend engineers who have experience in data engineering and who are passionate about making a global impact in sustainable building development. If you love wearing multiple hats and delivering new capabilities end to end, from first prototype to deployment, then you could be a strong fit.
We're looking for problem solvers who are excited to write high quality software to solve complex challenges with multimodal data. You might spend some of your time developing data schemas for multimodal data, building data orchestration, or scaling AI pipelines. You will be part of a talented, interdisciplinary engineering team helping build an AI platform from the ground up.
How you will make an Impact
  • Design and implement robust, end to end production-level software to enable inference, optimization, and other complex services as part of a SaaS offering
  • Build scalable representations of complex data types
  • Build data orchestration pipelines to manipulate geospatial, vector and text-based data
  • Collaborate with AI engineers to build scalable, reproducible deployment pipelines for machine learning models and AI services.
  • Work effectively with cross-functional teams of engineers, scientists, domain experts and PMs

What you should have:
  • Bachelor's degree in CS or equivalent practical experience
  • 5+ years of experience building large, complex software systems and applications
  • Experience working with messy and complex data sets and building data orchestration
  • Strong python development skills

It'd be great if you also had these:
  • Experience with infrastructure as code
  • Experience with GCP
  • Experience with geospatial data
  • Experience working in an early stage company with rapidly changing requirements

Apply