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Machine Learning Geospatial Jobs (NOW HIRING)

Onescreen

Founding Machine Learning Engineer

Boston, MA · On-site

They are seeking a Founding Machine Learning Engineer to own the development of matching algorithms ... Preferred : • Geospatial data experience (H3, PostGIS, GeoPandas) • Mobility or location data ...

Tive

Senior Data Scientist

Boston, MA · On-site

You will work at the intersection of AI, machine learning, geospatial intelligence, and real-world logistics data to build capabilities that turn raw signals into intelligence customers can act on.

Lynker Corporation

Geospatial Analyst

Charleston, SC · On-site +1

$55K - $72K/yr

Knowledge or experience related to land cover classification using machine learning or more ... sciences, geospatial analysis, information technology, resource management, conservation, and ...

Lynker Corporation

Geospatial Analyst

Charleston, SC · On-site +1

$55K - $72K/yr

Seeking a Geospatial Analyst to support the National Oceanic and Atmospheric Administration's (NOAA ... Knowledge or experience related to land cover classification using machine learning or more ...

The Marlin Alliance

Sr Machine Learning Engineer

San Diego, CA · On-site

$112K - $154K/yr

Required Skills and Experience: * 10+ years of experience as a data scientist, data engineer, geospatial engineer, machine learning engineer, or software engineer. * Proven experience developing and ...

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Machine Learning Geospatial information

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How much do machine learning geospatial jobs pay per hour?

As of Jul 18, 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 July 2026, with employment types broken down into 2% Locum Tenens, 73% Full Time, 3% Part Time, 2% Contract, 16% Nights, and 4% Summer. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $60,627 per year, or $29.1 per hour.
Founding Machine Learning Engineer

Founding Machine Learning Engineer

Onescreen

Boston, MA • On-site

Full-time

Re-posted 3 days ago

Job description

Job Summary:
Onescreen is a modern platform for out-of-home advertising, focused on streamlining the planning, buying, and measuring of OOH campaigns. They are seeking a Founding Machine Learning Engineer to own the development of matching algorithms and the data platform that supports them, along with designing and shipping models that optimize OOH inventory rankings.
Responsibilities:
• Design and ship matching and ranking models for OOH inventory: candidate generation, re-ranking, geospatial-aware scoring.
• Own the data warehouse layer end to end: staging, marts, feature pipelines, freshness, lineage.
• Stand up offline and online evaluation infrastructure — measure the gap between them, don't assume it.
• Publish ranking and matching APIs for product surfaces, with latency and quality SLOs.
• Instrument model monitoring: drift detection, prediction distribution, feature freshness, retraining triggers.
Qualifications:
Required:
• you have owned a production ranking, matching, or recommendation system end-to-end
• Strong production Python (NumPy, Pandas, FastAPI, SQLAlchemy)
• Strong SQL and modern data warehouse experience (BigQuery preferred)
• Real ranking and matching modeling fluency — learning-to-rank, retrieval and re-rank patterns, not just classification
• Evaluation methodology rigor: holdouts, leakage prevention, online vs. offline gap measurement
• Comfort owning the data pipeline as well as the model
• Bias toward shipping. Clear writer. Self-directed.
Preferred:
• Geospatial data experience (H3, PostGIS, GeoPandas)
• Mobility or location data experience
• Embedding-based retrieval (pgvector, FAISS, vector databases)
• Bandits, contextual bandits, or online learning
• A/B testing infrastructure design
• Causal inference
• dbt
• Ad-tech or OOH domain familiarity
Company:
Onescreen is the modern partner for out-of-home advertising. Founded in 2020, the company is headquartered in Boston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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