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Salary: USD 160,000 - 210,000 / annual
Treeswift builds physical AI systems for field workers in the energy sector, combining hardware (LiDAR, cameras, sensors), AI, and software to increase worker productivity by 10x. The company works with three of the five largest US utilities, helping them reduce wildfire risk, manage vegetation-related outages, and accelerate storm recovery.
As a Senior Robotics Engineer focused on Perception, you will drive technical outcomes across perception, mapping, sensor fusion, and calibration that directly support business expansion. Key responsibilities include:
- Improving product quality and consistency across diverse operating conditions by leading SLAM (Simultaneous Localization and Mapping) development, incorporating new sensor technologies, and developing innovative approaches for GPS-degraded environments.
- Accelerating time-to-delivery by advancing sensor fusion and calibration capabilities, including fusing multi-user, multi-session point cloud maps and automating alignment of LiDAR and imagery data to create consistent large-scale scenes.
- Enhancing power scalability and reliability by eliminating the need for extrinsic field measurements on vehicle sensors and improving data quality detection at the source.
You will work in a fast-paced, high-ownership environment with a team of roboticists from top institutions (Penn, Caltech, CMU) and enterprise software engineers from companies like Palantir and Stripe. The role is based in the New York office with additional offices in San Francisco and Philadelphia.
Required qualifications: 5-10 years of perception robotics experience, proficiency in C++ and Python, extensive SLAM development background (ideally ground-based LiDAR), and expertise in extrinsic and intrinsic sensor calibration including automated data-driven approaches. Prior experience with ultra-wide angle cameras is a plus.
The company values mission-first thinking, curiosity, continuous learning, and an ownership mindset where good ideas are encouraged from all levels.
