Responsibilities

• Lead the design and development of advanced machine learning models for autonomous driving tasks, including perception, decision-making, and control
• Drive end-to-end machine learning pipeline development from data collection and preprocessing to model training, optimization, and deployment
• Apply state-of-the-art deep learning techniques, such as reinforcement learning, imitation learning, and self-supervised learning, to improve autonomous driving performance
• Optimize model performance in real-world driving conditions and ensure seamless integration with the vehicle’s software stack
• Collaborate with cross-functional teams, including software, hardware, and vehicle control, to align machine learning systems with overall vehicle architecture
• Mentor junior engineers and provide guidance on best practices for machine learning development
• Stay updated on the latest trends and research in machine learning and autonomous driving, bringing innovative approaches to the team

Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, AI, Robotics, or a related field
• Extensive experience with deep learning algorithms (CNN, RNN, Transformer) and their applications in autonomous systems
• Strong proficiency in Python and machine learning frameworks (TensorFlow, PyTorch), with a proven track record of deploying models in real-world systems
• Deep understanding of reinforcement learning, imitation learning, and advanced optimization techniques
• Experience working with large-scale datasets and cloud-based machine learning pipelines
• Excellent leadership and communication skills, with a demonstrated ability to lead technical projects

Preferred Qualifications

• Strong background in autonomous driving technologies and end-to-end learning for self-driving cars
• Experience with hardware-in-the-loop (HIL) testing and real-time deployment
• Experience in research and development related to autonomous driving and robotics
• ROS1/ROS2 experience
• Experience deploying predictive models in real-world environments
• Inference optimization experience (TensorRT, CUDA programming, etc.)
• History of books/academic activities in related fields (CVPR, ICCV, ECCV, IROS, ICRA, etc.)