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Zürich, Zürich, Schweiz/Suisse/Svizzera/Svizra
Onsite
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
Our robots require precise and real-time localization, which they achieve by utilizing onboard sensors such as IMUs, lidar, cameras and GNSS. In environments without existing maps, the robot must dynamically create a map while simultaneously localizing itself within it. As our next SLAM Engineer on a growing team, you will be an expert in laser- and camera-based localization techniques and SLAM and enhance these capabilities. You will shape our robots’ ability to navigate with pinpoint precision, and you will be part of a team focused on enabling our robots to navigate autonomously. If you are passionate about robotics and driven to innovate in SLAM and localization, we encourage you to join us in shaping the future of intelligent robotics.
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.
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.
Experience with state of the art deep learning algorithms for SLAM and localization.
Publications at top-tier conferences.
Experience with ROS/ROS2.