Principal Engineer Motion Planning

Job description

Job responsibilities

• Lead the research and development of novel algorithms and sub-systems for motion planning in autonomous driving, thereby expanding the Operational Design Domain.
• Lead cross-functional projects to define new or upgrade existing interfaces to solve problems
• Monitor overall system performance to identify areas for improvement and develop technical strategies to address deficiencies
• Guide cross-functional project teams to provide comprehensive solutions and demonstrate the ability to think beyond the confines of the planning system.
• Architect and integrate complex combinations of motion planning and prediction algorithms, driving their evaluation and refinement for real-world deployment.
• Design and build a robust, scalable, and high-performance codebase that facilitates rapid exploration, prototyping, and rigorous evaluation of innovative motion planning approaches and algorithms.
• Drive technical collaboration and interface seamlessly with perception and prediction components upstream and trajectory optimization, tracking and control components downstream, ensuring end-to-end system performance.
• Leverage your deep software development and research expertise to teach others better software practices and principles, fostering a culture of technical excellence.

Job requirements

• PhD preferred in Robotics, Computer Science, Computer Engineering, Mechanical Engineering, or a related field; or a Master's degree with 7+ years of experience in the robotics (preferably AV industry).
• 10+ years of research experience in robotics / motion planning, with a proven track record of contributing to state-of-the-art solutions and leading significant projects.
• 5+ years of C++ software development, with an emphasis on developing high-performance and reliable systems.
• Past experience owning and leading technical development on complex features from problem formulation through research, implementation, and deployment in a production environment, demonstrating significant impact.
• Thirst for knowledge, continuous innovation, and a drive to push the boundaries of autonomous driving technology, acting as a technical thought leader.
• Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden Markov Models, and Particle Filters.
• Experience with machine learning techniques (such as Bayesian modeling and inference techniques) for decision making under uncertainty.
• Experience with the Bazel build framework