Staff AI Engineer - AMR Robot Localization

Job Description

The Robotics Software team is pioneering the next generation of autonomous robotic systems, focusing on autonomous mobile robots (AMRs) and intelligent robotic platforms. We develop full-stack robotics capabilities—from perception and planning to control and system integration—bringing innovative, real-world autonomous solutions to the future of the work. 

We are looking for a Staff Robot Localization Engineer to develop localization solutions that support the development, testing, and validation of localization subsystem of autonomous robotic systems. In this role, you will focus on developing camera- and LiDAR-based localization subsystem, design technical specification, creating and executing test plan, integrating the software with physical and simulation platforms, and enabling teams to accomplish the technical and business objectives.  

You will work cross-functionally with experts in autonomy, contributing to system-level validation and the continuous improvement of system robustness and validation workflows. 

What You'll Do:

• Design and implement high-precision localization methods using camera, LiDAR, wheel encoder and inertial sensors.  

• Develop scalable and real-time localization module optimized for autonomous robotic systems. 

• Create engineering specifications and test procedures to ensure system compliance. 

• Evaluate and benchmark the performance of localization systems. 

• Review the state-of-the-art in camera- and LiDAR-based localization algorithms  

• Troubleshoot using strong knowledge of probabilistic estimation, sensor fusion, and real-time system implementation.  

• Adjust and fine-tune localization system parameters to improve accuracy and robustness 

Your Skills & Abilities: 

• Bachelor's, Master’s or Ph.D. in Robotics, Computer Science, Electrical/Mechanical Engineering, or related field. 

• Proficiency in C++ or Python. 

• Expertise in camera- and LiDAR-based localization algorithms, statistical estimation theory, and practices such as pose graph and factor graph optimization and implementation. 

• Experience optimizing localization software to balance performance within resource constraints. 

• Understanding of state-of-the-art solutions in place recognition for addressing loop-closure detection issues. 

• Familiarity with ROS2 or other robotics middleware. 

• Understanding and experience of other robotics key modules such as perception, mapping, and path planning. 

• Adhere to continuous development and deployment practices in robotic software development 

What Will Give You A Competitive Edge: 

• Proficiency with deep learning frameworks and toolchains like PyTorch and TensorFlow 

• Knowledge of the development process for deep learning and AI models 

• Familiarity