Robotics Engineer; planning & behaviour

Robotics Engineer (planning & behaviour)

Department:
Behaviour | Job Type: Full-time |

Location:

Cambridge | Modality:
Hybrid |

Start Date:

19/01/2026

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.

Note:

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

• 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.
• Motion Planning & Control:
  Design and implement software that translates high‑level behavioral requests into executable, low‑level commands for various robotic platforms (e.g., vehicles running PX4).
• Heterogeneous Planner Integration:
  Integrate and manage a library of diverse motion planners for our heterogeneous team of robots, including navigation stacks (e.g., TEB planner) and manipulation planners (e.g., Move It).
• 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 geometric reasoning and feasibility checks to verify that actions are physically possible, providing critical feedback to the system to trigger adaptive responses.

• 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, Mu Jo Co , SAPIEN, Gazebo are also relevant).
• Reinforcement learning and automated planning (e.g. using PDDL).
• Extensive, hands‑on experience with ROS 2 and standard planning frameworks within its ecosystem (e.g., Nav2, Move It).
• Familiarity with collision checking, obstacle avoidance techniques, and geometric data structures used in planning.
• Excellent, production‑quality programming skills in C++ and Python.

• Practical experience with symbolic AI planners or formal planning languages like PDDL.
• Experience in multi‑agent planning, task allocation, or auction‑based mechanisms.
• A background in trajectory optimization or optimal control theory.
• Experience developing custom controllers and planners for real‑world robotic hardware.