About this role

Responsibilities

- Design, implement, tune, and improve path and motion planning algorithms for safe, smooth, and continuous robot motion in large-scale, dynamic environments.

- Develop perception algorithms using both deep learning and classical geometric computer vision.

- Implement, tune, and maintain localization and mapping (SLAM) algorithms, applying state-of-the-art ML approaches where they add value.

- Perform sensor selection and evaluation across lidar, cameras, and ToF sensors, balancing performance against cost and other constraints.

- Develop and maintain calibration algorithms for the above sensors.

- Help shape the autonomy roadmap by identifying technical gaps and risks, proposing prioritized initiatives, and translating them into milestones

- Build tooling and metrics to test, evaluate, and continuously improve fleet performance, including regression detection and proactive service triggers.

- Own high-quality software engineering practices: version control (GitHub), code review, CI/CD, and maintaining build and deployment pipelines.

- Work cross-functionally with hardware, firmware, applications, and operations teams to develop, ship, and scale new products.

Requirements

- Master’s degree in Robotics, Computer Science, or a related field (or equivalent experience).

- 4 or more years developing production robotics or autonomy software.

- Strong proficiency in ROS, C++ and Python in a Linux environment.

- Hands-on experience implementing and tuning algorithms in two or more of: perception, path/motion planning, localization/mapping, sensor calibration.

- Experience with deploying deep learning models

- Solid software engineering fundamentals: Git, code review, CI/CD, and build pipeline maintenance.

- Practical experience working with real sensor data from lidar, cameras, and/or ToF sensors.

- Demonstrated ability to work cross-functionally and ship to real hardware.

Nice to Haves

- Familiarity with cutting-edge ML approaches to localization, mapping, and perception (e.g., object detection and tracking, learned features, visual place recognition).

- Experience with cloud-based or collaborative/lifelong SLAM systems.

- Experience with containerization and deployment tooling

- A track record of staying current with robotics and AI research and bringing new ideas into practice.

Compensation: Based on experience and qualifications.