Senior Motion Planning Engineer

Role responsibilities:

• Lead the research and development of novel algorithms for motion planning in autonomous driving, including but not limited to advanced search-based methods, sophisticated geometry-based methods, and decision making under uncertainty with a strong emphasis on probabilistic approaches.
• 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.
• Guide and mentor junior team members, cultivating a culture of product-focused engineering, rigorous research, and advanced development.

What we're looking for:

• PhD preferred in Robotics, Computer Science, Computer Engineering, Mechanical Engineering, or a related field; or a Master's degree with 2+ years of experience in the robotics (preferably AV industry)
• 2+ years of research experience in robotics / motion planning, with a proven track record of contributing to state-of-the-art solutions
• 2+ 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.
• Thirst for knowledge, continuous innovation, and a drive to push the boundaries of autonomous driving technology.

Skills appreciated

• Experience with probabilistic models, including but not limited to Gaussian mixture models, Hidden Markov Models, and Particle Filters.
• Experience with Bayesian modeling and inference techniques for decision making under uncertainty.
• Experience with the Bazel build framework