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).

Machine Learning Research Engineer