AeroVect 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 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
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
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
