Job Description

About the Team
The Applied Machine Learning (AML) - Enterprise team provides machine learning platform products on VolcanoEngine with cloud native resource scheduling system which intelligently orchestrates different tasks and jobs with minimised costs of every experiment and maximised resource utilisation, rich modelling tools including customised machine learning tasks and web IDE, and multi-framework high performance model inference services.

In 2021, through VolcanoEngine, we released this machine learning infrastructure to the public, to provide more enterprises with reduced costs of computation power, lower barriers to machine learning engineering and deeper developments in AI capabilities.

Responsibilities
-Responsible for the development and performance optimization of the Volcano Engine large model training and inference systems, including but not limited to model computation optimization, tuning thousand-GPU training clusters, distributed LLM inference systems, and large-scale inference traffic scheduling.
-Responsible for solving technical challenges related to high concurrency, high reliability, and high scalability, supporting Volcano Engine’s daily training and inference traffic at the scale of hundreds of billions of tokens.
-Responsible for researching and introducing forward-looking technical architectures for large model training and inference, including but not limited to subgraph matching, compiler optimization, and model quantization.
-Responsible for integrating heterogeneous hardware with training and inference frameworks, including but not limited to GPUs, NPUs, and TPUs.
-Focused on improving compute utilization across globally distributed ultra-large-scale GPU clusters through elastic scheduling, GPU oversubscription, task orchestration, and related techniques.
-Collaborate closely with algorithm teams to jointly optimize algorithms and systems.