Research Engineer in Large Model System

Leveraging substantial data and computing resources and through continued investment in these domains, we have developed a proprietary general-purpose model with multimodal capabilities.

In the Chinese market, Doubao models power over 50 ByteDance apps and business lines, including Doubao, Coze, and Dreamina, and is available to external enterprise clients via Volcano Engine.

Today, the Doubao app stands as the most widely used AIGC application in China.

Responsibilities - design and development of the architecture of large-scale machine learning systems, solving technical difficulties such as high concurrency, high reliability, and high scalability of the system.

- Covering various sub-directions of machine learning system, including resource scheduling, model training, model inference, data management, and workflow orchestration.

- Responsible for the research and introduction of advanced technologies in machine learning systems, such as the latest hardware architecture, heterogeneous computing systems, and compiler-based optimization technologies.

- Working closely with the algorithm teams to optimize the algorithm and system jointly.

Minimum Qualifications - has research or technical backgrounds in LLM, code generation, large pre-trained models - Candidates with pre-training foundation technologies, including efficient training and pretraining as a service Preferred Qualifications - Candidates with top-tier conference papers, including NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, KDD, etc., relevant internship experience or winners of ACM competitions - Proficient in deep learning frameworks such as PyTorch and TensorFlow, and programming languages such as Python or Java.