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: 1. Responsible for ensuring our ML systems are operating and running efficiently for large model development, training, evaluation, and inference 2. Responsible for the stability of offline tasks/services in multi-data center, multi-region, and multi-cloud scenarios 3. Responsible for resource management and planning, cost and budget, including computing and storage resources 4. Responsible for global system disaster recovery, cluster machine governance, stability of business services, resource utilisation improvement and operation efficiency improvement 5. Build software tools, products and systems to monitor and manage the ML infrastructure and services efficiently 6. Be part of the global team roster that ensures system and business on-call support