Job Description

We are seeking a talented Software Engineer to develop advanced navigation and path planning algorithms that enable our global fleet of autonomous cleaning robots to move safely and intelligently in complex environments. This role is ideal for an engineer passionate about robotics, algorithm development, and system integration. You will take ownership of the planning stack, from high-level route planning, to complex complete path generation. This role demands strong fundamentals in motion planning, computational geometry, and optimization, as well as hands-on experience deploying algorithms on real robots. You will work closely with teams across SLAM, Perception, and Control to deliver navigation software that performs reliably in the real world.

Key Responsibilities

• Design and implement global path planning algorithms for large-scale dynamic environments, ensuring optimal and collision-free routing across multi-floor indoor environments

• Develop and optimize complete coverage path planners tailored for cleaning robots

• Develop and maintain costmap and environment representations, integrating perception and SLAM inputs for accurate planning

• Develop and apply learning-based planning techniques (e.g. reinforcement and imitation learning) to enhance navigation adaptability and robustness in dynamic environments

• Develop comprehensive test cases for validating algorithms and software in both simulated and real-world environments, to ensure reliable performance.

• Maintain comprehensive documentation of all code implementations and test cases

Qualifications & Experience

• Bachelor’s or Master’s degree in CS, Robotics, or a related field (or PhD with relevant focus).

• 3+ years of industry experience in navigation, motion or coverage planning.

• Strong proficiency in C++ and Python development on Linux.

• Hands-on experience with ROS1 / ROS2 and modern software engineering practices.

• Solid understanding of robot kinematics, dynamics, and map representations (occupancy grids, costmaps, graph maps).

• Deep understanding of graph and sampling-based global planners (A*, D*, RRT*, PRM, etc.).

• Experience with implementing complete coverage path planning algorithms.

• Familiarity with trajectory optimization and local planning frameworks (TEB, MPC, or similar).

• Familiarity with simulation tools (Gazebo, Isaac Sim, or RViz) for development and validation.

• Strong analytical mindset with excellent problem-solving and debugging abilities.

• Experience with learning-based or hybrid planning algorithms (e.g. reinforcement and imitation learning) is a bonus.

• Experience with optimizing software for embedded hardware (e.g., Jetson, ARM) or GPU-based acceleration is a bonus.

If you have a passion for driving meaningful operational improvements, excel at analytical problem-solving, and thrive in a dynamic scaleup atmosphere, we invite you to join LionsBot and help shape the future of robotics innovation.