Who We Are

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 will

• 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

You Have

• 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

We Prefer

• 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