Senior Software Engineer, Behavior Planning

🕒 3 dias atrás

🌐 Reino Unido, Itália, +6 outros países – Remoto

infoinfo

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

👻 Score fantasma 25%

infoinfo

🗣️🇺🇸🇬🇧 Inglês obrigatório

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AeroVect

11 - 50 funcionários

📦 Logística

💼 Consultoria

🚀 Aeroespacial

💰 Seed Round em 2022-07

Logistics • Consulting • Aerospace

A AeroVect é uma empresa de tecnologia que se especializa em automação avançada para Equipamentos de Apoio em Solo (GSE) na indústria da aviação. O principal produto da empresa, o AeroVect Driver, é um sistema de direção autônoma altamente escalável e agnóstico em relação ao OEM, projetado para melhorar a segurança, a produtividade e a sustentabilidade nas operações de manuseio em solo. Confiada por companhias aéreas e operadores líderes, a AeroVect pretende revolucionar as operações em áreas de tráfego aéreo com sua tecnologia de direção autônoma de primeira linha, desenvolvida no Vale do Silício para o mercado global de aviação.

Descrição

• Design and build behavior planning systems for autonomous driving in structured, low-speed environments • Own the design and implementation of key behavior-planner modules that determine vehicle actions in complex, dynamic airside scenarios • Address multi-agent interaction modeling, rule-based and learned decision-making, and airport ground-operation edge cases • Develop and implement advanced behavior planning algorithms for autonomous vehicles • Collaborate with cross-functional teams to integrate planning systems robustly • Design, write, and maintain efficient, scalable C++ and Python code • Contribute to behavior-planning software architecture and continuous improvement • Test algorithms extensively in simulated environments and real-world scenarios • Analyze system performance and implement data- and feedback-driven enhancements • Maintain documentation for code, algorithms, and system designs • Coordinate development with other engineering teams

🎯 Requisitos

• 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 • Minimum of 3 years of industry experience in autonomous driving, robotics, or a related field • 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 • Experience implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment • Experience ensuring 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

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