Senior Software Engineer, Behavior Planning

This is a fully remote position, open to applicants in United Kingdom, +7 more countries.

📋 Description

• Design and develop behavior planning systems for autonomous vehicles operating in structured, low-speed environments.

• Take ownership of the design and implementation of essential behavior-planner modules that dictate vehicle actions in intricate and dynamic airside situations.

• Tackle multi-agent interaction modeling, employing both rule-based and learned decision-making approaches, while addressing edge cases in airport ground operations.

• Create and execute advanced behavior planning algorithms tailored for autonomous vehicles.

• Collaborate with cross-functional teams to ensure robust integration of planning systems.

• Design, code, and maintain efficient and scalable applications in C++ and Python.

• Contribute to the software architecture of behavior-planning systems and promote continuous enhancement.

• Conduct comprehensive testing of algorithms in both simulated environments and real-world applications.

• Assess system performance and implement improvements driven by data and feedback.

• Maintain thorough documentation for code, algorithms, and system designs.

• Coordinate development efforts with other engineering teams.

⛳️ Requirements

• Proficient in modern C++ (11/14/17) and skilled in object-oriented programming.

• Experienced in Python for rapid prototyping and testing purposes.

• Strong capabilities in debugging, profiling, and optimizing code.

• In-depth knowledge of behavior planning algorithms, including state machines, behavior trees, and probabilistic planning.

• Familiarity with path planning algorithms such as A*, RRT, or optimization-based strategies.

• At least 3 years of professional experience in autonomous driving, robotics, or a related domain.

• Understanding of state machines, behavior trees, and decision-making processes under uncertainty.

• Expertise in path planning algorithms like A*, D*, and Rapidly-exploring Random Trees (RRT).

• Familiarity with machine learning techniques, particularly in behavior prediction and planning contexts.

• Experience with ROS / ROS2.

• Proven track record of implementing systems capable of re-planning at high frequencies to adapt to dynamic environmental changes.

• Expertise in ensuring behavior planning algorithms execute with minimal latency for real-time navigation.

• Proficient in optimization techniques and probabilistic models to facilitate informed planning decisions under uncertainty.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible working hours and remote work options.

• Opportunities for professional development and continuous learning.

• A dynamic and collaborative work environment.