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AeroVect is a Series A autonomous vehicle company transforming ground handling operations for airlines and ground service providers globally. We are seeking a Senior Software Engineer to design and build behavior planning systems for autonomous vehicles operating in structured, low-speed airport environments.
In this role, you will own the design and implementation of key modules in the behavior planner—the decision-making layer that determines vehicle actions in complex, dynamic airside scenarios. You'll work at the intersection of mission-level goals and motion-level execution, tackling multi-agent interaction modeling, rule-based and learned decision-making, and edge cases unique to airport ground operations. This is a deeply technical position reporting to the Planning Tech Lead, working closely with the autonomy engineering team.
Responsibilities:
- Develop and implement advanced behavior planning algorithms for autonomous vehicles
- Design, write, and maintain efficient, scalable code in C++ and Python
- Collaborate with cross-functional teams to ensure robust integration and functionality of planning systems
- Contribute to the architecture and continuous improvement of behavior planning software
- Conduct extensive testing in simulated and real-world scenarios to validate and refine 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
Requirements:
- Proficient in modern C++ (11/14/17) and object-oriented programming
- Skilled in Python for rapid prototyping and testing
- Strong debugging, profiling, and code optimization skills
- 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 related field
- Minimum 3 years of industry experience in autonomous driving, robotics, or related field
Preferred:
- Knowledge of state machines, behavior trees, and decision-making under uncertainty
- Expertise in path planning algorithms (A*, D*, RRT)
- Machine learning techniques for behavior prediction and planning
- ROS / ROS2 experience
- High-frequency replanning systems for dynamic environments
- Low-latency behavior planning for real-time navigation
- Optimization techniques and probabilistic models
- Master's or PhD in Robotics, AI, Mathematics, or related field with focus on planning, optimization, or control theory
