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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 problems in multi-agent interaction modeling, rule-based and learned decision-making, and robust handling of edge cases unique to airport ground operations. This is a deeply technical position that shapes a market-defining enterprise product combining autonomous vehicle technology with a robotics-as-a-service business model. You will report to the Planning Tech Lead and collaborate closely with the autonomy engineering team.
Key responsibilities include:
- 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 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 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
Preferred qualifications:
- 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 behavior prediction and planning
- Experience with ROS / ROS2
- Experience implementing systems that re-plan at high frequencies to adapt to dynamic environmental changes
- Ensuring behavior planning algorithms execute with minimal latency for real-time navigation
- Proficiency in optimization techniques and probabilistic models for planning under uncertainty
- Master's degree or PhD in Robotics, AI, Mathematics, or a related field with focus on planning, optimization, or control theory
