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Geospatial Machine Learning Engineer Jobs (NOW HIRING)

Oxman

Machine Learning Research Engineer

New York, NY · On-site

$142K - $184K/yr

EDEN is a digital design environment for engineering and designing ecosystems, modeling the flows ... Develop machine learning models for geospatial inference of key ecosystem metrics, leveraging ...

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 ...

NextGenEnergyJobs

$77K - $105K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

Compunnel

Machine Learning Engineer

Philadelphia, PA · On-site

The Machine Learning Engineer will design, develop, and deploy production-ready machine learning solutions with a strong focus on hands-on engineering and coding. The role requires extensive Python ...

Planet

Senior Machine Learning Engineer

Arlington, VA · On-site

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

Planet Labs PBC

Senior Machine Learning Engineer

Arlington, VA · On-site

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

Planet

Senior Machine Learning Engineer

Arlington, VA · On-site +1

$120K - $165K/yr

Planet's Analytics Team for Global Monitoring focuses on novel geospatial and time series analytics ... As a Senior Machine Learning Engineer, you will drive hands-on engineering and modeling for Defense ...

The Marlin Alliance

Sr Machine Learning Engineer

San Diego, CA · On-site

$110K - $152K/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 ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

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Geospatial Machine Learning Engineer information

See salary details

$5

$46

$90

How much do geospatial machine learning engineer jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for geospatial machine learning engineer in the United States is $46.63, according to ZipRecruiter salary data. Most workers in this role earn between $35.82 and $57.69 per hour, depending on experience, location, and employer.

What is the difference between Geospatial Machine Learning Engineer vs GIS Analyst?

AspectGeospatial Machine Learning EngineerGIS Analyst
Required CredentialsBachelor's/Master's in GIS, Computer Science, or related; experience with machine learningBachelor's in Geography, GIS, or related; proficiency in GIS software
Work EnvironmentTech-focused, data science teams, software developmentMapping, spatial data analysis, urban planning
Industry UsageTech companies, environmental agencies, researchGovernment, urban planning, environmental consulting
Search & Comparison IntentFocus on advanced spatial data modeling with MLFocus on spatial data management and analysis

The main difference is that Geospatial Machine Learning Engineers develop models using machine learning techniques to analyze spatial data, while GIS Analysts focus on managing, mapping, and analyzing geographic information using GIS software. Both roles require GIS knowledge, but the engineer role emphasizes programming and ML skills for complex data insights.

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

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

Infographic showing various Geospatial Machine Learning Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $96,989 per year, or $46.6 per hour.

Machine Learning Research Engineer

New York, NY • On-site

Oxman
Specialized Design Services • 11 - 50 employees
Learn more about Oxmanexternal

$142K - $184K/yr

Full-time

Re-posted 8 days ago

Job description

OXMAN
OXMAN is a nature-based research and design company based in Manhattan. We incubate ventures and technologies that reimagine the relationship between humanity and the natural world. Working across disciplines-from architecture and ecology to materials science and computation, we develop nature-centric solutions to critical environmental challenges.
EDEN
Nature provides humanity with services that are critical for survival: the sequestration of carbon, the filtration of water, and the production of the air we breathe. EDEN works to strengthen and regenerate these natural processes by cultivating biodiverse, resilient ecosystems that sustain life for all species-human and non-human alike.
EDEN is a digital design environment for engineering and designing ecosystems, modeling the flows, relationships, and processes that sustain them. We build tools that quantify how landscapes can be engineered to achieve specific performance goals, cooling cities, filtering water, sequestering carbon, and protecting key species, and use them to guide the design of ecologically active sites.
One hectare of well-designed landscape can sequester up to four times the annual emissions of an average home, filter enough water to support thirteen neighborhoods, and reduce ambient temperatures by more than ten degrees. EDEN enables designers to plan intentionally for these outcomes through analysis, simulation, and optimization, turning ecological function into an actionable design parameter.
Our design team works directly with clients to apply these tools toward site-specific goals, from logistics campuses and residential communities to rewilding and climate-resilient developments. Together with our clients, we are designing biodiverse, productive environments that serve both humanity and nature.
Key Responsibilities
  • 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 simulation.
  • Develop and refine advanced deep generative models and reinforcement learning algorithms for built-environment design.
  • Contribute to decision-making frameworks that combine procedural generation with ML and data-driven optimization.
  • Collaborate with computational ecologists and data scientists to integrate generative design with ecosystem simulation models.
  • Align design outputs with ecological performance indicators such as species richness and carbon sequestration.
  • Prepare detailed technical documentation and contribute to model validation using empirical ecological data.

Key Goals and Outcomes
  • Research and development of high-fidelity Geospatial AI models for the automated inference of ecosystem metrics across varied scales.
  • Utilize inferred geospatial data to drive the computational synthesis and design of functional, resilient ecosystems.
  • Establish a robust pipeline for integrating remote sensing and geospatial data into generative design workflows.
  • Deliver scalable ML frameworks that provide real-time or near-real-time feedback on ecological performance (e.g., carbon sequestration and biodiversity).
  • Develop innovative design methods that support and enhance ecological processes through data-driven optimization.

Required Experience
  • Proven experience developing and deploying geospatial machine learning models, deep generative models, or RL algorithms in practical research problems.
  • Ph.D. or equivalent experience in Computer Science, Machine Learning, Operations Research, or related fields.
  • Demonstrated experience working in cross-functional teams bridging ML research with ecology, architecture, or design.

Preferred Experience
  • Experience with GIS tools and remote sensing technologies for geospatial analysis.
  • Prolific corpus of digital or physical expressions rooted in process-driven research and design.
  • Industry experience combined with a background in leading research and producing striking work.

Technical Skills
  • Commitment to Nature-centric principles and a willingness to integrate technology and ecology.
  • Enthusiasm for pushing boundaries in design and science with innovative thinking.
  • Self-directed with an aptitude for nurturing collaborative teamwork across disciplines
Required Education/Certifications
  • Ph.D. in a relevant field (CS, ML, OR).

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