Senior Software Engineer, Motion Planning

AeroVect is seeking a Senior Software Engineer for Motion Planning to develop and implement advanced behavior planning algorithms for autonomous vehicles. This role involves collaborating with cross-functional teams, designing and maintaining efficient C++ and Python code, and contributing to software architecture. The engineer will conduct extensive testing in simulated and real-world environments, analyze system performance, and ensure seamless coordination with other engineering teams to transform ground handling with autonomy.

Must Have

• Develop and implement advanced behavior planning algorithms for autonomous vehicles
• Collaborate with cross-functional teams for robust integration of planning systems
• Design, write, and maintain efficient and scalable C++ and Python code
• Contribute to the architecture and continuous improvement of behavior planning software
• Conduct extensive testing in simulated and real-world scenarios
• Analyze system performance and implement enhancements
• Maintain comprehensive documentation of code, algorithms, and system designs
• Work closely with other engineering teams for seamless coordination
• 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 (state machines, behavior trees, probabilistic planning)
• Familiarity with path planning algorithms (A*, RRT, 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

Good to Have

• 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 for behavior prediction and planning
• Experience with ROS / ROS2
• Implementing systems that can re-plan at high frequencies
• Ensuring behavior planning algorithms execute with minimal latency for real-time navigation
• Proficiency in optimization techniques and probabilistic models
• Master’s degree or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory

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