Machine Learning Research Engineer

Department: EDEN

Location: NYC Office

Compensation: $142K – $235K

Employment Type: FullTime

Overview

OXMAN is a nature-based research and design company based in Manhattan. We incubate nature-centric ventures that address critical challenges at the intersection of Nature and Humanity. Working across sectors, OXMAN fosters innovation that reimagines the relationship between the built environment and the natural world.

EDEN is OXMAN’s applied ecological systems initiative operating at architectural, urban, and landscape scales. We are pioneering a digital design environment for engineering biodiverse, resilient ecosystems by developing software that enables designers and engineers to integrate ecosystem services—including carbon sequestration, thermal buffering, and biodiversity—into master planning and design. Our tools aim to transform ecosystem services from undervalued byproducts into essential infrastructure. While we test and refine the platform through client projects in master planning, architecture, and landscape architecture, our core mission is to build a powerful, adaptable system for designing productive, regenerative environments. We are looking for talented individuals to join us in shaping the future of nature-centric design.

EDEN is seeking a Machine Learning Research Engineer to join our interdisciplinary design team in New York City to support our mission of delivering stellar, data-driven design proposals that leverage EDEN’s unique suite of ecosystem engineering tools.

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).