<p>The Machine Learning (ML) System sub-team combines system engineering and the art of machine learning to develop and maintain massively distributed ML training and Inference system/services around the world, providing high-performance, highly reliable, scalable systems for LLM/AIGC/AGI In our team, you'll have the opportunity to build the large scale heterogeneous system integrating with GPU/NPU/RDMA/Storage and keep it running stably and reliably, enrich your expertise in coding, performance analysis and distributed system, and be involved in the decision-making process. You'll also be part of a global team with members from the United States, China and Singapore working collaboratively towards unified project direction. Responsibilities: - Responsible for developing and optimizing LLM inference framework. - Responsible for GPU and CUDA Performance optimization to create an industry-leading high-performance LLM inference engine.</p> <p>Minimum Qualifications: - Bachelor's degree or above, major in computer/electronics/automation/software, etc. - Proficient in C/C++, proficient in algorithms and data structures, familiar with Python - Understand the basic principles of deep learning algorithms, be familiar with the basic architecture of neural networks and understand deep learning training frameworks such as Pytorch. Preferred Qualifications: - Proficient in GPU high-performance computing optimization technology on CUDA, in-depth understanding of computer architecture, familiar with parallel computing optimization, memory access optimization, low-bit computing, etc. - Familiar with TensorRT-LLM, ORCA, VLLM, etc. - Knowledge of LLM models, experience in accelerating LLM model optimization is preferred.</p>