Responsibilities

• Develop and deploy navigation algorithms for robots operating in complex indoor environments
• Own localization, mapping, and SLAM pipelines running in real time on embedded compute
• Build planning frameworks for terrain traversal and dynamic obstacle avoidance
• Integrate navigation with the wider autonomy stack, including perception and motion control
• Build data collection and evaluation infrastructure to benchmark performance across environments
• Validate in simulation and close the sim-to-real gap through hardware iteration

Requirements

• Strong foundations in estimation theory, probabilistic robotics, and motion planning
• Hands-on experience with SLAM, planning, or autonomous navigation systems on real robots, gained through industry or research work
• Proficient in Python and C++, with ROS or equivalent middleware
• Experience getting systems working end-to-end on physical hardware, not only in simulation
• Able to debug across the hardware-software boundary and work through ambiguous problems
• Candidates with limited industry experience are welcome to apply, provided this is supported by strong relevant academic or research work, such as a thesis, publications, or hands-on robotics projects

Tyson Jay Management Pte Ltd | EA License No.: 24C2479 

Ivan Lim | EA Personnel No.: R1109856