The client is building products that predict and simulate Earth systems and how they interact with the built environment. They believe mapping and measuring changes to the human and natural world will help them manage climate-related risks. The goal is to enable users to understand how mitigation actions would shift their risk profile.
Responsibilities:
Develop Machine Learning (ML) and physics-based models for Earth systems applications where Machine Learning (ML) can bring hockey stick growth.
Present findings to team members, internal and external stakeholders, and help set a direction for future development.
Design and run Machine Learning (ML) simulations for environmental phenomena such as wildfires.
Fetch and pre-process geospatial and multimodal data in a highly parallel environment.
Create and maintain Client’s Cloud infrastructure for software development and high-volume production systems.
Mandatory:
Experience with earth engine and functional programming.
Experience with machine learning systems, algorithms, or applications such as: deep learning, computer vision,
Completed coursework in calculus, linear algebra, and probability, or their equivalent.
Experience with one or more general purpose programming languages, including but not limited to: Python
Experience with open-source tools such as git, TensorFlow, etc.
Experience with tiffs and tiff projections into physical spaces (aka GeoTiff) would be a plus.
Experience with using Machine Learning (ML) on image files and images that change over time would be a plus.
Skills:
Earth Systems Applications
Earth Engine
Machine Learning
Python
GeoTiff
Geospatial
Education:
Bachelor’s Degree or equivalent practical experience.
About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com.
US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
