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Machine Learning Research Engineer
| Company | OXMAN |
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| Location | New York, NY, USA |
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| Salary | $75000 – $225000 |
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| Type | Full-Time |
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| Degrees | PhD |
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| Experience Level | Senior, Expert or higher |
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Requirements
- 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.
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.
Preferred Qualifications
No preferred qualifications provided.
