SLAM Software Engineer

RIVR, part of Amazon

RIVR, part of Amazon is a robotics company pioneering Physical AI through real-world doorstep delivery. Founded in 2024 as an ETH Zurich spin-off, RIVR developed wheeled-legged robots designed to operate in complex, unstructured environments such as stairs, gates, doors, and uneven urban terrain. We believe that achieving general physical intelligence requires solving real customer problems in the real world, where robots can learn from rich operational data at scale. Following our acquisition by Amazon in March 2026, we are continuing this mission with greater reach and speed. By combining custom robot hardware, onboard autonomy, and cloud-based coordination, RIVR, part of Amazon is building the next generation of safe, reliable autonomous robots for last-mile delivery.

Responsibilities

• Develop state-of-the-art, online and offline localization and SLAM algorithms by fusing information from cameras, LiDARs, IMU, GNSS, and other sensors.
• Design, validate, and improve algorithms on challenging real-world data.
• Contribute to the dynamic mapping of the environment using data continuously gathered from ongoing robot deployments.
• 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.

Qualifications

• Master’s degree in a relevant field such as Robotics, Machine Learning, Computer Science, or a similar discipline.
• 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.

Preferred Qualifications

• Experience with state of the art deep learning algorithms for SLAM and localization.
• Publications at top-tier conferences.
• Experience with ROS/ROS2.