We are looking for a Robotics Engineer to join our Behaviour Team. You will build the core planning and action-execution capabilities of the Mastermind system. This role is responsible for the full stack of robotic behaviour, from high-level strategic planning down to the interaction with low-level motion control sub-processes. You will architect and implement both a task manager, which determines the multi-agent strategy to achieve a mission, and a behaviour manager, which translates those strategies into collision-free physical motion for our fleet of robots.

From the 3 layers of robotic behavior, this position is mostly to work in the behaviour (i.e. symbolic planning layer) while touching a little of motion (path) planning and control.

• behaviour planning
• motion (path) planning
• control

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Note: you will not need to touch or implement low level proportional or PID controllers.

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Who we are

About the Role

What you’ll get to do

• Strategic Task Planning: Design and implement multi-agent planning algorithms that decompose complex mission goals into a sequence of high-level tasks for the robotic team. Integrate and manage a library of diverse navigation stacks (e.g. Nav2) to plan and execute paths and perform complex tasks. You’ll be building hierarchical behavior trees that decompose complex multi-agent tasks into smaller components.
• Motion single-robot Planners: use motion planners for programming individual (single robot) navigation stacks (e.g., TEB planner) and manipulation planners (e.g., MovIt).
• Action Abstraction: Define a clear hierarchical interface between high-level strategic tasks and the low-level robotic actions required to execute them (e.g. using PDDL).
• Feasibility and Validation: Implement the geometric reasoning and feasibility checks to verify that actions are physically possible, providing critical feedback to the system to trigger adaptive responses.

What we’d like to see

• MSc or PhD in Robotics, Computer Science, or a related engineering field.
• A strong, demonstrable background in robotics planning, spanning both high-level task planning and low-level motion planning, control, and kinematics.
• Experience with simulation tools and workflows (Isaac Lab preferably, MuJoCo, SAPIEN, Gazebo are also relevant).
• Reinforcement learning , behaviour trees, finite state machines and automated planning (e.g. using PDDL).
• Extensive, hands-on experience with ROS 2 and standard planning frameworks within its ecosystem (e.g., Nav2, MoveIt).