The Role
Develop advanced generative models and reinforcement learning algorithms to optimize designs enhancing ecological processes bridging human and natural environments.
Summary Generated by Built In
OXMAN
OXMAN is a hybrid Design and R&D company that fuses design, technology, and biology to invent multi-scale products and environments. The fusion of disciplines within our work opens previously impossible opportunities within each domain—allowing design to inspire science and science to inspire design.
At OXMAN, we question dominant modes of design that have divorced us from Nature by prioritizing humanity above all else (human-centric design). Although it is design that has caused this rift, we believe that design also offers the greatest opportunity to heal it. We propose a Nature-centric approach that delivers design solutions by, for, and with the natural world, while advancing humanity.
In this pursuit, we reject all forms of segregation and instead call for a radical synergy between human-made and Nature-grown environments. This approach demands that we design across scales for systems-level impact. We consider every designed construct a whole system of heterogeneous and complex interrelations—not isolated objects—that are intrinsically connected to their environments. In doing so, we open ourselves up to moving beyond mere maintenance toward the advancement of Nature.
Summary
OXMAN is seeking a Research Engineer with expertise in Deep Reinforcement Learning, Deep Generative Modeling, and Data-Driven Design Optimization to join our interdisciplinary team. Leveraging advanced computational techniques, you will develop innovative design methods that support and enhance ecological processes, bridging human-made and natural environments.
In this role, you will explore the intersection of generative design methods and data-driven optimization strategies.
Your primary objective will be creating approaches that integrate generative design, ecosystem simulation, and optimization techniques to enhance ecosystem services benefiting both humans and natural biodiversity. You will develop and test generative models and procedural techniques for designing environments, behaviors, and scenarios. These designs will be evaluated and refined through data-driven optimization and reinforcement learning methods, aiming to maximize positive ecological outcomes, such as biodiversity and resilience.
Please provide cover letter and portfolio if available.
Responsibilities
- Develop and refine advanced deep generative models and reinforcement learning algorithms to generate, explore, and optimize built-environment design strategies aimed at enhancing ecosystem services.
- Create decision-making frameworks that combine procedural generation with machine learning and data-driven optimization, improving interactions between built and natural environments.
- Investigate and implement interfaces between procedural generation techniques, machine learning approaches, and deep generative modeling.
- Collaborate with computational ecologists to integrate generative design frameworks with ecosystem simulation models, producing architectural and infrastructural designs that interact positively with natural environments.
- Apply optimization and reinforcement learning techniques to align generative design outputs with ecological performance indicators, such as species richness, carbon sequestration, and water management.
- Collaborate with data scientists and ecologists to incorporate extensive, diverse datasets (remote sensing, climate data, biodiversity records) into generative and optimization methodologies.
- Contribute to model validation by comparing simulated results to empirical ecological data, ensuring accuracy and reliability.
- Prepare detailed technical documentation of methods, assumptions, and implementations to support reproducibility and knowledge sharing.
Qualifications
- Ph.D. or equivalent experience in Computer Science, Machine Learning, Operations Research, or related fields.
- Proven experience developing and deploying deep generative models, reinforcement learning algorithms, and data-driven optimization methods in practical design problems.
- Strong knowledge in mathematical modeling, probabilistic methods, simulation techniques, procedural modeling, and complex systems.
- Proficiency in handling and analyzing large, heterogeneous datasets (environmental, climate, remote sensing) using Python, C++, or similar languages.
- Experience with GIS tools and remote sensing technologies for geospatial analysis.
- Demonstrated ability to work in cross-functional teams, bridging machine learning research with ecology, architecture, engineering, and design.
- Enthusiasm for pushing boundaries in design and science; ability to merge rigorous computational methods with innovative thinking.
- A commitment to Nature-centric principles and willingness to explore novel ways of integrating technology and ecology.
OXMAN does not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, or any other legally protected characteristics.
NYC Salary Range: $75,000-$225,000
Salary is based on a number of factors including job-related knowledge, skills, experience, and other business and organizational needs. Our compensation package also includes variable compensation in the form of year-end bonuses, benefits, immigration assistance, and equity participation.
Top Skills
C++
Gis Tools
Python
Remote Sensing Technologies
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The Company
What We Do
Envision a future of complete synergy between Nature and humanity, where human-made and Nature-grown become indistinguishable.
OXMAN is a new kind of company fusing design, technology, and biology to invent multi-scale products and environments—allowing design to empower science and science to empower design. As activist designers, we call for a fundamental shift from human-centric design to Nature-centric design. Categorical delineations between climate change and global pandemics, loss of biodiversity and loss of empathy for one another give way to authoring systems that address manifold challenges. Through this shift, we open ourselves up to moving beyond mere maintenance of Nature toward its radical betterment
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