Senior/Staff Autonomy Software Generalist

We’re looking for an autonomy software generalist to work across the full robotics stack — perception, localization, mapping, and planning — while collaborating closely with hardware, firmware, applications, and operations teams. This is a broad role suited to someone who thrives on solving problems end-to-end, from algorithm design to fleet-scale deployment, and who stays energized by the fast pace of advances in robotics and AI.

• 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.

• 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.

• 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.