Job Description

Hybrid 3 days at office

We are looking for a

Senior Motion Planning Engineer

to design and implement the core algorithms that enable autonomous vehicles to make safe, efficient, and human-like driving decisions in dynamic and uncertain environments. In this role, you will work at the intersection of decision-making, trajectory generation, and control, collaborating closely with perception, prediction, map generation and controls teams to build a reliable, real-time motion planning system for self-driving.

Design, develop, and optimize motion planning algorithms that handle complex, interactive traffic scenarios

Formulate and implement trajectory generation methods that balance safety, comfort, and efficiency under real-world driving conditions

Research and apply state-of-the-art planning methods, including optimization-based approaches, probabilistic decision-making, reinforcement learning, and imitation learning

Integrate perception, prediction, and mapping outputs into planning pipelines for robust decision-making

Ensure real-time performance of planning algorithms on automotive-grade embedded hardware

Contribute to the development of closed-loop validation pipelines, including simulation, software-in-the-loop, hardware-in-the-loop, and on-road vehicle testing

Collaborate with multidisciplinary teams (perception, prediction, map generation, controls, systems) to integrate planning algorithms into the self-driving autonomy stack

Stay current with advances in motion planning, decision-making, and learning-based approaches for autonomous driving

Drive engineering excellence by writing clean, efficient, and well-tested code

Requirements

MSc/PhD in Robotics, Computer Science, Electrical or Mechanical Engineering, or a related field with 5+ years of relevant industry experience Strong background in motion planning, trajectory optimization, and decision-making methods (e.g., A, optimization-based planning, graph search etc.)
Experience with reinforcement learning, imitation learning, or deep learning approaches for planning and control Proficiency in Python and C++, with familiarity in ML frameworks such as PyTorch or TensorFlow Solid understanding of vehicle dynamics, kinematics, and control theory Hands-on experience with real-time systems, performance optimization, and deployment on embedded automotive hardware Skilled in simulation-based development and validation using tools such as CARLA or MATLAB/Simulink Strong mathematical foundation in optimization, linear algebra, probability, and statistics Excellent problem-solving skills and ability to work in fast-paced, collaborative environments Nice to have: Prior experience in self-driving or ADAS development and familiarity with functional safety standards (ISO 26262, SOTIF)
Job Type: Full-time

Pay: 60.000,00€ - 85.000,00€ per year