Robotics Engineer (planning & behaviour)

PassFort; Cambridge (MA); Hybrid; USD 85,000 - 115,000; Full time

Job summary

PassFort is seeking a Robotics Engineer to join our Behaviour Team in Cambridge. This role involves building core planning and action-execution capabilities for robotic systems. You will design algorithms and manage a library of motion planners, translating high-level requests into executable commands.

The ideal candidate will hold an MSc or PhD in a relevant field and possess extensive experience in robotics planning. A hybrid work modality is offered along with a dynamic team environment.

Qualifications

• Solid background in robotics planning at both high and low levels.
• Hands-on experience with simulation tools such as Isaac Lab and Gazebo.
• Excellent programming skills in C++ and Python.

Responsibilities

• Design and implement multi-agent planning algorithms.
• Translate high-level behavioral requests into executable commands.
• Integrate and manage diverse motion planners.

Skills

Robotics planning

C++

Python

Motion planning

Reinforcement learning

ROS 2

Simulation tools

Obstacle avoidance techniques

Education

MSc or PhD in Robotics, Computer Science, or a related field

Tools

PX4

PDDL

Isaac Lab

MuJoCo

SAPIEN

Gazebo

Job description

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.

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.
• 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., MoveIt).
• 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.

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 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).
• Familiarity with collision checking, obstacle avoidance techniques, and geometric data structures used in planning.
• Excellent, production‑quality programming skills in C++ and Python.

What will set you apart

• 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.