Job Description

Team Introduction:
Data AML is ByteDance's machine learning middle platform, providing training and inference systems for recommendation, advertising, CV (computer vision), speech, and NLP (natural language processing) across businesses such as Douyin, Toutiao, and Xigua Video.

AML provides powerful machine learning computing capabilities to internal business units and conducts research on general and innovative algorithms to solve key business challenges. Additionally, through Volcano Engine, it delivers core machine learning and recommendation system capabilities to external enterprise clients.
Beyond business applications, AML is also engaged in cutting-edge research in areas such as AI for Science and scientific computing.

Research Project Introduction:
Large-scale recommendation systems are being increasingly applied to short video, text community, image and other products, and the role of modal information in recommendation systems has become more prominent. ByteDance's practice has found that modal information can serve as a generalization feature to support business scenarios such as recommendation, and the research on end-to-end ultra-large-scale multimodal recommendation systems has enormous potential. It is expected to further explore directions such as multimodal co-training, 7B/13B large-scale parameter models, and longer sequence end-to-end based on algorithm-engineering CoDesign.

Engineering research directions include:
- Representation of multimodal samples
- Construction of high-performance multimodal inference engines based on the PyTorch framework
- Development of high-performance multimodal training frameworks
- Application of heterogeneous hardware in multimodal recommendation systems

Algorithmic research directions include:
- Design of reasonable recommendation-advertising and multimodal cotraining architectures
- Sparse Mixture of Experts (Sparse MOE)
- Memory Network
- Hybrid precision techniques