Responsibilities
- Develop and implement advanced behavior planning algorithms for autonomous vehicles
- Collaborate with cross-functional teams to ensure robust integration and functionality of planning systems
- Design, write, and maintain efficient and scalable code in C++ and Python
- Contribute to the architecture and continuous improvement of behavior planning software
- Conduct extensive testing in simulated environments and real-world scenarios to validate and refine behavior planning algorithms
- Analyze system performance and implement enhancements based on data and feedback
- Maintain comprehensive documentation of code, algorithms, and system designs
- Work closely with other engineering teams to ensure seamless coordination and development
Requirements
- Proficient in modern C++ (11/14/17) and object-oriented programming
- Skilled in Python for rapid prototyping and testing
- Strong in debugging, profiling, and optimizing code
- Deep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planning
- Familiarity with path planning algorithms like A*, RRT, or optimization-based methods
- Master’s degree in Computer Science, Robotics, or a related field
- Minimum of 3 years of industry experience in autonomous driving, robotics, or a related field
Nice to Have
- Knowledge of state machines, behavior trees, and decision-making under uncertainty
- Expertise in path planning algorithms such as A*, D*, and Rapidly-exploring Random Trees (RRT)
- Knowledge of machine learning techniques, especially in the context of behavior prediction and planning
- Experience with ROS / ROS2
- Implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment
- Ensuring that behavior planning algorithms can execute with minimal latency for real-time navigation
- Proficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertainty
- Master’s degree or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory is a plus
