PhysicalAIEngineer(Model)

WE ARE LOOKING FOR THE BEST AD Division의 Physical AI Engineer (Model)는 Generative AI 기술을 실제 로봇 및 모빌리티 시스템의 의사결정과 제어로 연결하는 역할을 수행합니다. 차세대 End-to-End Trajectory Generation 및 Decision-making Model 개발에 참여하며, 복잡하고 동적인 환경에서도 안전하고 효율적인 주행이 가능한 Physical AI 시스템을 구축합니다. 또한 Reinforcement Learning(RL), Imitation Learning(IL), Motion Planning 기술을 융합하여 Autonomous Driving AI의 성능을 향상시키고 실제 차량 환경에 적용 가능한 모델을 개발합니다. The Physical AI Engineer (Model) in the AD Division bridges generative AI technologies with real-world robotic and mobility actuation systems. This role focuses on developing next-generation end-to-end trajectory generation and decision-making models capable of safe and efficient operation in complex and dynamic environments. You will integrate reinforcement learning, imitation learning, and advanced motion planning techniques to improve autonomous driving AI performance and deploy scalable physical AI solutions. Responsibilities - End-to-End Trajectory Generation 및 Decision-making Model 개발을 위한 데이터 전처리, 모델 학습 및 성능 검증 수행 - Model-Based Reinforcement Learning(MBRL) 및 Imitation Learning(IL) 알고리즘 개발 및 최적화 - Motion Planning, Filtering(Kalman Filter, Particle Filter 등), Navigation 알고리즘 통합 및 검증 - CUDA 기반 딥러닝 모델 및 Planning Pipeline 최적화를 통한 On-device Real-time 성능 확보 지원 - Simulation 및 실제 차량 환경에서 AI 모델 및 알고리즘 검증 수행 - Perception, Planning, Control 팀과 협업하여 차세대 Physical AI 시스템 개발 - Develop data preprocessing pipelines, train models, and conduct performance validation for end-to-end trajectory generation and decision-making models - Develop and optimize Model-Based Reinforcement Learning (MBRL) and Imitation Learning (IL) algorithms - Integrate and validate motion planning, filtering (e.g., Kalman Filter, Particle Filter), and navigation algorithms - Support on-device optimization of deep learning models and planning pipelines using CUDA to achieve real-time performance - Validate AI models and algorithms in simulation and real-world vehicle environments - Collaborate with perception, pl