Software Engineer in Machine Learning Systems

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 stable and reliable, 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 - Development of machine learning systems, including key computing development, task scheduling, and machine learning system management and operation; - Deployment of machine learning services; - Online serving of machine learning models; - Iterate and develop the system using customer-driven scenarios. Minimum Qualifications - Master distributed, parallel computing principles; know the recent advances in computing, storage, networking, and hardware technologies; - Familiar with the state-of-the-art machine learning algorithms and mainstream platforms (e.g., Tensorflow, Pytorch, MxNet); - Master at least one or two programming languages in Linux environment such as C/C++, Go, Python, etc; Preferred Qualifications - Experience in GPU-based high-performance computing.