SLAM Software Engineer

- 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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RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one.

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.

Key job 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.