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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 an experienced 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.
Key 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 of planning systems
- Contribute to architecture and continuous improvement of behavior planning software
- Conduct extensive testing in simulated and real-world scenarios to validate algorithms
- Analyze system performance and implement enhancements based on data and feedback
- Maintain comprehensive documentation of code, algorithms, and system designs
- Coordinate closely with other engineering teams for seamless development
Requirements:
- Proficiency in modern C++ (11/14/17) and object-oriented programming
- Skilled in Python for rapid prototyping and testing
- Strong debugging, profiling, and code optimization capabilities
- 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 Qualifications:
- Knowledge of state machines, behavior trees, and decision-making under uncertainty
- Expertise in path planning algorithms (A*, D*, RRT)
- Machine learning techniques, especially 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 for planning under uncertainty
- Master's or PhD in Robotics, AI, Mathematics, or related field with focus on planning, optimization, or control theory
