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Machine Learning Geospatial Jobs in New York (NOW HIRING)

Oxman

Machine Learning Research Engineer

New York, NY · On-site

$142K - $224K/yr

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 ...

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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 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.
What are popular job titles related to Machine Learning Geospatial jobs in New York? For Machine Learning Geospatial jobs in New York, the most frequently searched job titles are:
What job categories do people searching Machine Learning Geospatial jobs in New York look for? The top searched job categories for Machine Learning Geospatial jobs in New York are:
What cities in New York are hiring for Machine Learning Geospatial jobs? Cities in New York with the most Machine Learning Geospatial job openings:
Machine Learning Geospatial jobs near you
Machine Learning Research Engineer

Machine Learning Research Engineer

Oxman

New York, NY • On-site

$142K - $224K/yr

Full-time

Posted 23 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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