SLAM Software Engineer, Rivr

RIVR Technologies AG; Zürich; Vor Ort; CHF 110.000 - 160.000; Vollzeit

Zusammenfassung

RIVR Technologies AG in Zürich, Switzerland, is seeking a seasoned SLAM Engineer to advance real-time localization and mapping for autonomous delivery robots.

You will fuse data from cameras, LiDAR, IMU, GNSS, and build deployment-ready code optimized for embedded platforms, with a focus on safety and reliability.

Qualifikationen

• 3+ years of industry or research experience.
• Background in computer vision, robotics or autonomous driving with SLAM/LiDAR focus.
• Strong mathematical fundamentals (linear algebra, probability, optimization).
• Production-level C++ and prototyping in Python.
• Experience deploying SLAM/localization on hardware.

Aufgaben

• Develop online and offline localization and SLAM algorithms using multi-sensor data.
• Validate algorithms on real-world data and challenging environments.
• Contribute to dynamic mapping and ground-truth data collection for evaluation.
• Assist in sensor calibration systems and deployment-ready code for robots.

Kenntnisse

SLAM

Localization

Robotics

Mathematics

Python

C++

Software prototyping

Ausbildung

Master's degree in Robotics / CS

Tools

ROS/ROS2

Git

Linux

Jobbeschreibung

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.

Basic Qualifications

• 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.

Preferred Qualifications

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