Machine Learning Engineering Team Lead

About Treeswift:

In the face of rising threats, increasing pressure on affordability, and unprecedented demand for power, Treeswift empowers energy companies to modernize their field work to meet the growth and challenges ahead.

We build physical AI for the field worker: whether on foot or in a vehicle, our technology is an ironman suit for engineers, linemen, and vegetation crews: same worker, same boots on the ground, now operating at 10x productivity. Our platform is powered by cutting edge hardware, sensors (LiDAR, camera, etc…), AI and software designed to revolutionize work in the toughest environments.

Our technology has enabled our customers to reduce wildfire risk, regulatory and outage risk from vegetation, avoid delays and cost overruns in new construction, and accelerate recovery from severe storms.

To tackle this challenge, we are bringing together a team of mission-driven experts with deep industry experience in robotics (Penn, Caltech, CMU) and enterprise software development (Palantir, Stripe, Oracle, MongoDB). We have raised funding from leading investors including Penny Pritzker’s Inspired Capital.

We are headquartered in midtown Manhattan, with additional offices in San Francisco and Philadelphia.

Our growth is only accelerating. We’re looking for deeply curious and highly ambitious people who want to have a real world impact. Come build the future of (field) work with us.

About the role

ML sits at the heart of Treeswift’s product translating complex sensor data into meaningful insights that drive critical decision-making for our customers. As our business grows the importance of this work only grows with it.

You will join at a critical inflection point and will lead the charge on the following priorities (amongst others):

• Technical Leadership. You will be responsible for continuing to improve the quality and consistency of our models across a growing breadth of operating conditions. As the breadth of models and datasets grows you will thoughtfully introduce feedback loops and ML Ops that accelerate model improvement. You have excellent judgement on what problems require fast experimentation/validation, and where we should be focused on sustainable scalable improvement over a longer period of time.

• Edge model development. Working closely with our robotics and embedded teams, you will support growing exploration of model deployment on devices for real-time use cases.

• Lead and grow our team (as needed). We have an incredibly talented ML team distributed across our NYC, Philly and SF offices. You will lead the current team, identify gaps and grow the team as needed.

• Collaborate closely with our Software, Robotics, Hardware and Product teams to deliver on Treeswift’s vision and business objectives.

You will report to our Head of Software Engineering. This is a full-time, hybrid role based out of our Lower Manhattan, NYC office (2-3 days per week in person, currently pinned to Tuesdays and Wednesdays).

About Machine Learning At Treeswift

Machine Learning at Treeswift is the layer that translates sensor data into meaning for our end-users. At a high-level we develop three categories of model that power downstream business decisions.

• Classifying a point cloud - distinguishing ground from vegetation from structures etc.

• Identifying individual objects within 3D space — poles, equipment, wires, trees, etc.

• Describing characteristics of individual objects. This includes everything from tree species and health, detailed wire types, pole information (height, attachments, etc.) etc.

Together this information informs critical decision-making for our customers such as:

• What vegetation is a risk to utility infrastructure and how severe is that risk? What action is recommended as a result (trim, removal, etc…)?

• Is ongoing asset construction deviating from the expected design and if so how? If so, is this an approved deviation or a quality issue that needs to be addressed before a downstream customer or financial impact?

Required skills and experience

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