
AeroVect
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Who We Are
AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. 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. For more information, visit www.aerovect.com.
You will
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Develop and implement advanced behavior planning algorithms for autonomous vehicles
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Collaborate with cross-functional teams to ensure robust integration and functionality of planning systems
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Design, write, and maintain efficient and scalable code in C++ and Python
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Contribute to the architecture and continuous improvement of behavior planning software
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Conduct extensive testing in simulated environments and real-world scenarios to validate and refine behavior planning algorithms
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Analyze system performance and implement enhancements based on data and feedback
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Maintain comprehensive documentation of code, algorithms, and system designs
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Work closely with other engineering teams to ensure seamless coordination and development
You Have
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Proficient in modern C++ (11/14/17) and object-oriented programming
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Skilled in Python for rapid prototyping and testing
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Strong in debugging, profiling, and optimizing code
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Deep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planning
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Familiarity with path planning algorithms like A*, RRT, or optimization-based methods
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Master’s degree in Computer Science, Robotics, or a related field
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Minimum of 2 years of industry experience in autonomous driving, robotics, or a related field
We Prefer
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Knowledge of state machines, behavior trees, and decision-making under uncertainty
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Expertise in path planning algorithms such as A*, D*, and Rapidly-exploring Random Trees (RRT)
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Knowledge of machine learning techniques, especially in the context of behavior prediction and planning
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Experience with ROS / ROS2
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Implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment
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Ensuring that behavior planning algorithms can execute with minimal latency for real-time navigation
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Proficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertainty
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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
Apply now
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