Senior Robotics Engineer (Motion Planning) - Contractor

Institute and research centre

Role overview

Key responsibilities

• Lead model-based behavior, path, and trajectory planning using search, sampling, and constrained optimization.
• Address vehicle constraints, static and moving obstacles, repeated replanning, and application-specific terrain requirements.
• Define project scope, interfaces, acceptance criteria, and technical milestones with client teams; communicate risks and limitations.
• Integrate planners with perception, localization, control, and simulation systems; support simulator and vehicle testing.
• Lead architecture, development, and maintenance of modular planning software with documented interfaces and reusable C++ and Python components.
• Oversee code reviews, automated testing, CI/CD, dependency management, and versioned releases.
• Mentor engineers and students, collaborate on learning-based and hybrid planning, and contribute to research and publications.

Required qualifications

• Master's degree or Ph.D. in Robotics, Control, Engineering, Computer Science, Applied Mathematics, or a related field.
• Substantial hands-on motion-planning experience and technical ownership of software integration and delivery.
• Strong expertise in path planning, trajectory optimization, numerical methods, and constrained optimization.
• Understanding of vehicle kinematics and dynamics, optimal control, and planning-control interfaces.
• Strong C++ and practical Python skills; experience with ROS/ROS 2 and robotic simulation.
• Experience evaluating planners in closed loop and diagnosing integrated-system failures.
• Ability to translate client needs into implementable requirements, mentor engineers, and communicate technical results clearly in English.
• Experience leading robotics software development, including architecture, Git collaboration, automated testing, CI/CD, release management, and maintainability.

Desirable experience

• Graph-based and sampling-based methods such as A*, D*, and RRT* are desirable.
• OSQP, acados, CasADi, or similar optimization tools are desirable.
• Off-road or terrain-aware planning, uncertainty-aware methods, and autonomous-vehicle platform experience are desirable.
• Docker and learning-based or hybrid motion-planning experience are desirable.