Senior Software Engineer, Motion Planning

Senior Software Engineer, Motion Planning 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 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

Seniority level Mid‑Senior level

Employment type Full‑time

Job function Other

Industries Airlines and Aviation

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Location: Frankfurt, Germany

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