Senior Software Engineer (Behavior Planning) - Autonomous Driving
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AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers operate globally. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers.
We are looking for an experienced Senior Software Engineer who can design and build best-in-class behavior planning systems for autonomous driving in structured, low-speed environments.
In this role, you'll own the design and implementation of key modules in the behavior planner—the decision-making layer that determines what the vehicle should do 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 opportunity offers a deeply technical engineer the chance to shape a market-defining enterprise product that combines autonomous vehicle technology with a robotics-as-a-service (RaaS) business model. This role reports to our Planning Tech Lead and works closely with the autonomy engineering team.
You Will
- 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 behavior planning 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 and development
You Have
- 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
We Prefer
- 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 the context of behavior prediction and planning
- Experience with ROS / ROS2
- Implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment
- Ensuring that behavior planning algorithms can execute with minimal latency for real-time navigation
- Proficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertainty
- Master’s degree or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory is a plus
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AeroVect
View Company ProfileAeroVect is a pioneering aviation technology company that is fundamentally revolutionizing airport logistics through advanced autonomous mobility. Headquartered in San Francisco, California, the company specializes in transforming traditional ground support equipment (GSE)—such as baggage tractors and cargo loaders—into fully autonomous, self-driving fleets. Under the hood, AeroVect leverages state-of-the-art artificial intelligence, computer vision, and high-precision LiDAR technology to navigate the highly complex, bustling, and unpredictable environments of modern airport tarmacs safely. Their primary target audience spans massive global airlines, airport authorities, and ground handling enterprises that are desperately seeking to overcome severe labor shortages, enhance operational safety, and dramatically increase turnaround efficiency. What sets AeroVect apart in the rapidly evolving autonomous vehicle sector is its hyper-focused, domain-specific application—ignoring public roads to perfectly master the highly controlled but demanding domain of aviation logistics, thereby delivering immediate, quantifiable ROI to the aerospace supply chain.
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