Senior Software Engineer, Motion Planning

Who We AreAeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.You willDevelop and implement advanced behavior planning algorithms for autonomous vehiclesCollaborate with cross-functional teams to ensure robust integration and functionality of planning systemsDesign, write, and maintain efficient and scalable code in C++ and PythonContribute to the architecture and continuous improvement of behavior planning softwareConduct extensive testing in simulated environments and real-world scenarios to validate and refine behavior planning algorithmsAnalyze system performance and implement enhancements based on data and feedbackMaintain comprehensive documentation of code, algorithms, and system designsWork closely with other engineering teams to ensure seamless coordination and developmentYou HaveProficient in modern C++ (11/14/17) and object-oriented programmingSkilled in Python for rapid prototyping and testingStrong in debugging, profiling, and optimizing codeDeep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planningFamiliarity with path planning algorithms like A*, RRT, or optimization-based methodsMaster’s degree in Computer Science, Robotics, or a related fieldMinimum of 3 years of industry experience in autonomous driving, robotics, or a related fieldWe PreferKnowledge of state machines, behavior trees, and decision-making under uncertaintyExpertise 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 planningExperience with ROS / ROS2Implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environmentEnsuring that behavior planning algorithms can execute with minimal latency for real-time navigationProficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertaintyMaster’s degree or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory is a plus