- Performance Analysis & Optimization: Deeply analyze and optimize GPU applications to identify and eliminate performance bottlenecks, including memory access patterns, thread scheduling, and execution efficiency. - Algorithm development: Develop and implement efficient GPU-accelerated algorithms using parallel computing frameworks such as CUDA or DirectX to improve the processing speed of compute-intensive tasks. - Provide technical guidance and training to the team, sharing best practices and optimization tips for GPU programming. - Continuously monitor GPU technology and industry trends, evaluate and integrate new technologies to improve system performance.

- Bachelor's or Master's degree or above in Computer Science, Electronic Engineering, or related field. - At least 5 years of experience in GPU programming and performance optimization, with a strong background in CUDA or DirectX development. - Proficient in C/C++ programming with in-depth knowledge of computer architecture and parallel computing principles. - Proficient in the use of GPU performance analysis and debugging tools, with actual performance optimization cases and results. - Experience analyzing and tuning the performance of various AI/HPC workloads. Bonus points: - Relevant research experience in the field of high-performance computing (HPC). - In-depth GPU optimization experience with machine learning frameworks such as TensorFlow or PyTorch. - Hands-on experience using Orin or Xavier platforms for AI model deployment and optimization, as well as developing algorithms related to autonomous driving. - Experience with NVIDIA GPU and CUDA programming.