Assist in the creation of robust sensor calibration systems that perform reliably in complex and unpredictable environments.
Support the development of an efficient workflow to accurately capture ground truth data, and maps of deployment sites for algorithm evaluation.
Contribute to the implementation of deployment-ready code for the real robot, optimized for the robot’s computational constraints.
Create and maintain documentation and best practices to streamline knowledge sharing.
A minimum of 3 years of industry or research experience.
Background in computer vision, robotics or autonomous driving, with experience in areas such as 3D visual or LiDAR SLAM, place recognition, structure from motion, filtering, or Bayesian estimation.
Strong mathematical fundamentals including linear algebra, vector calculus, probability theory, and mathematical optimization.
Ability to write production-level code in modern C++, and prototype efficiently in Python.
Experience with deploying SLAM or localization algorithms on hardware platforms.
Master’s degree in a relevant field such as Robotics, Machine Learning, Computer Science, or a similar discipline.
Experience with state of the art deep learning algorithms for SLAM and localization.
Publications at top-tier conferences.
Experience with ROS/ROS2.
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