Key Responsibilities:

1.End-to-End Model Design & Innovation: Lead the architecture design, implementation, and optimization of next-generation End-to-End autonomous driving models, covering the full stack from perception, prediction, and decision-making to planning.

2.Generative AI Exploration: Develop and implement E2E algorithm solutions leveraging state-of-the-art generative AI technologies, including VLM/VLA (Vision-Language/Action Models), LLMs (Large Language Models), and Diffusion Models.

3.Inference Optimization & Deployment: Spearhead model inference acceleration and performance tuning. Drive model lightweighting strategies for embedded platforms to resolve high latency and compute bottlenecks in real-world deployments.

4,Data Engine & Evaluation: Participate in the development of automated data pipelines and establish robust model evaluation metrics. Continuously iterate and enhance E2E model performance through closed-loop data mining.

5.Cross-functional Collaboration: Collaborate closely with system architects and embedded engineering teams to ensure seamless model integration and deployment. Address practical performance challenges and optimize model effectiveness in real-world road testing.

6.Frontier Tech Research: Stay on the bleeding edge of large-scale models and reinforcement learning (RL). Explore the migration and application of these advanced technologies within commercial vehicle driving scenarios.

Qualifications & Requirements:

Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, Information Engineering, Electronic Engineering, Robotics, or a related field.

Experience: 3+ years of hands-on experience in deep learning algorithm development, ideally within the autonomous driving or robotics industry. A strong publication record at top-tier AI/CV conferences (e.g., CVPR, ICCV, NeurIPS) or proven success in relevant open-source projects is highly preferred.

Technical Expertise:

1.Proficient in Python and C++, with extensive practical experience using deep learning frameworks such as PyTorch or TensorFlow.

2.Deep understanding of mainstream algorithms used in perception, prediction, and planning/control.

3.Solid grasp of Imitation Learning (IL) and Reinforcement Learning (RL) principles.

4.Strong experience in model inference optimization, covering model compression techniques (pruning, distillation) and quantization (INT8/FP16). Familiarity with high-performance inference frameworks like TensorRT, ONNX Runtime, or vLLM is a major advantage.

5.Problem Solving: Excellent logical thinking, systematic troubleshooting skills, and a robust foundation in data structures and algorithms. A genuine passion for autonomous driving technology and a drive to tackle challenging real-world engineering issues.

6.Communication: Fluent in both English and Mandarin, capable of effectively collaborating with global headquarters and local cross-functional teams.