Senior Software Engineer, Behavior Planning

Role overview This role focuses on building behavior planning software for autonomous vehicles operating in structured, low-speed airside environments such as airport ground operations. The senior engineer will own key modules of the decision-making layer that turns mission goals into motion-level actions, balancing rule-based logic, learned behavior, and robust handling of multi-agent edge cases. The work combines deep robotics and motion-planning research with production-grade C++ and Python engineering on a market-defining autonomy product.

Responsibilities - Design, implement, and continuously improve behavior planning algorithms for autonomous vehicles operating in dynamic, multi-agent airside scenarios. - Develop efficient, scalable code in modern C++ and Python, including rapid prototyping tools and production-quality modules. - Validate algorithms through extensive simulation testing and real-world trials, analyzing performance data and refining behavior based on feedback. - Collaborate with perception, motion planning, integration, and other engineering teams to ensure seamless end-to-end autonomy functionality. - Maintain thorough documentation of code, algorithms, and system designs, and contribute to the long-term architecture of the planning stack. - Track and apply advances in decision-making under uncertainty, probabilistic planning, and high-frequency re-planning techniques.

Requirements - Proficiency in modern C++ (11/14/17) and object-oriented programming, with strong Python skills for prototyping and testing. - Solid debugging, profiling, and code-optimization abilities. - Deep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planning methods. - Familiarity with path planning techniques including A*, RRT, or other optimization-based approaches. - Master's degree in Computer Science, Robotics, or a closely related field. - At least 3 years of industry experience in autonomous driving, robotics, or an adjacent discipline.

Nice to have - Experience with ROS or ROS2, machine learning for behavior prediction, or optimization and probabilistic models for real-time planning under uncertainty. - Background implementing high-frequency re-planning systems with minimal latency for real-time navigation. - Master's or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory.