About the job
About Treeswift:
Facing challenges from severe storms, wildfires, and the demand for affordable energy solutions, Treeswift is dedicated to helping energy companies transform their field operations to tackle unprecedented growth and challenges. Our innovative sensors are deployed in the field, collecting extensive LiDAR and imagery data, which is then analyzed by our advanced AI models to provide actionable insights through our web platform. To date, our technology has empowered utilities to mitigate wildfire risks, streamline regulatory compliance, and accelerate recovery from severe weather events.
Since launching our services to utilities in June 2024, we have partnered with three of the five largest utilities in the United States and are rapidly expanding our customer base. We are building a team of passionate experts with extensive backgrounds in robotics from prestigious institutions like Penn, Caltech, and CMU, along with experience in enterprise software development from companies such as Palantir, Stripe, and Oracle. Our mission is supported by leading investors, including Penny Pritzker’s Inspired Capital.
Headquartered in lower Manhattan, Treeswift also maintains an office in Philadelphia, with team members located closer to customer sites in the Bay Area.
About the Role:
We are seeking a talented and driven Machine Learning Engineer to join our dynamic team. In this pivotal role, you will develop and implement cutting-edge machine learning solutions to advance our mission. The ideal candidate will have a strong background in training and deploying models within commercial environments. If you are an enthusiastic engineer with a desire to shape the future of distributed infrastructure management, we encourage you to apply.
This position is full-time and offers a hybrid work arrangement, requiring in-person attendance two days a week at our NYC office.
Key Responsibilities:
Drive the development of innovative machine learning models. Your work will focus on enhancing the efficiency and accuracy of energy infrastructure fieldwork, specifically through the development of LiDAR point cloud models for landscape and infrastructure classification, as well as image models for assessing vegetation attributes such as species and health.

