Senior Software Engineer, Motion Planning

Senior Software Engineer, Motion Planning

Join to apply for the Senior Software Engineer, Motion Planning role at AeroVect.

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

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Airlines and Aviation

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