Software Engineer

Hyphae is a mission-driven organization. Our vision is to break down disciplinary silos and conventions to redefine the relationship between nature, humans and their built environment.

We understand that the only way to effectively improve overall ecosystem health and resilience is from a systems thinking approach that respects the interconnectedness of everything. Our process bridges cross-disciplinary divides. We engage in diverse projects across scales that have a unique potential for innovation and catalyzing change. Improving conditions for human health must come with a reduced impact on the environment. Our experience in ecology, health-driven urban planning, architecture, applied scientific research, industrial design, and software development underscores our commitment to design solutions that are mutually beneficial for all.

We're building a technology platform to empower communities impacted by environmental injustice and climate-change to take control and determine their own future, a new approach to community-led, evidence-based urban revitalization. What previously required teams of engineers, epidemiologists, academics, government agencies and consultants can now be done by and for communities themselves, creating resilience, while re-localizing power, knowledge and capital in the communities.

As a Hyphae team member, you will be involved in researching, developing and deploying parametric and deep-learning models for generalizable regressions of environmental variables purposed for optimizing human health outcomes of built-environment interventions. You will report to our Chief Science Officer and be responsible for training, evaluation and deployment of deep learning models, and development of parametric analysis and design automation tools.

Responsibilities:

Training deep learning models for regression of higher dimensional data from lower dimensional inputs.

Writing scripts for data organization and training automation

Research and develop new deep learning and parametric process automation approaches.

Implement process automation for environmental analysis, design and modeling.

Preferred skills include:

Familiarity with computational geometry and linear algebra.

Experience with deep learning pipelines including but not limited to CGAN, transformers, diffusion models, and instance segmentation.

Expertise with python for process automation and data science.

Familiarity with LIDAR, point cloud data, and 3d modeling mesh data.

Understanding of computational fluid dynamic principles and experience with OpenFoam

Understanding of software deployment at scale

This is a full time position with competitive salary, benefits, vacation and flexible schedule. This position is hybrid remote.