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.
Requirements
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.