What You'll Do
Architect and refine the core planning components—mission, behavior, and motion—for autonomous ground support vehicles operating in airport airside zones. You'll shape the technical direction of the planning stack by selecting appropriate algorithms, defining system interfaces, and establishing scalable software architecture. Explore and validate new planning methodologies, including optimization-driven approaches, search-based strategies, and machine learning-enhanced pipelines, advancing the most effective solutions into production.
Work closely with teams specializing in perception, controls, localization, and systems engineering to integrate planning capabilities into a cohesive autonomous system. Champion software quality, functional safety, and robust systems engineering practices within the planning domain. Guide and support fellow engineers through technical mentorship, code reviews, and collaborative design discussions.
Requirements
- Hold a Bachelor’s or Master’s degree in Computer Science, Mathematics, Electrical or Mechanical Engineering, Robotics, Physics, or a closely related discipline.
- Possess proven experience building and deploying planning modules for autonomous systems in real-world or production environments.
- Demonstrate strong proficiency in C++ or Python, with hands-on work in Linux-based development environments.
- Show the ability to independently tackle open-ended technical challenges and deliver well-structured, maintainable solutions.
- Communicate effectively, take initiative, and exhibit a strong sense of accountability.
Preferred Qualifications
- Advanced degree (MS or Ph.D.) in Computer Science, Robotics, or a related field.
- Deep expertise in Modern C++ (C++14 or later) and coding standards relevant to safety-critical systems, such as MISRA or ISO 26262.
- Five or more years of industry experience focused on planning for robotic ground vehicles.
- Familiarity with distributed communication frameworks like ROS, ROS2, or other DDS-based middleware.
- Background in fast-moving startup environments, with a track record of adapting to shifting priorities and timelines.
Technical Environment
The development stack includes C++, Python, Linux, ROS and ROS2, DDS, trajectory optimization, decision-making under uncertainty, real-time constrained optimization, and hybrid planning pipelines combining classical and learning-based methods.
