Job Description

Team Introduction:
The ByteDance Recommendation Architecture Team is responsible for the design and development of the recommendation system architecture for ByteDance's related products. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.

Project Challenges:
1. Strategy Management and Optimization:
Build an intelligent system to achieve standardized definition of recommendation strategies, long-term and offline evaluation, automatic identification and retirement of ineffective strategies, and removal of related code configurations.
2. Adaptive Tuning and Fault Diagnosis:
Leverage large model capabilities to optimize parameters and configurations of systems and underlying components for diverse business loads in recommendation systems. Explore adaptive fault diagnosis solutions to provide global perspective for fault tracking, localization, and analysis.
3. Cost-Efficiency Balance:
Address the high costs of model training and operation when applying generative technologies to recommendation systems, balancing costs and efficiency to achieve effective recommendation within limited resources.
4. Cross-Domain Data Processing:
Handle massive heterogeneous data in horizontal cross-domain scenarios (e.g., e-commerce), improve and ensure data quality and accuracy, standardize data supply for cross-domain recommendation models, and enable low-cost cross-terminal services. Meanwhile, ensure data privacy, security, and compliance.
5. Data Storage and Quality Enhancement:
Develop low-cost, high-performance storage engines, design flexible Schema Evolution mechanisms, achieve high-concurrency real-time data writing and training-inference consistency. Deeply explore the quantitative relationship between data quality and model prediction performance, and build data-model correlation analysis tools and automated training data processing pipelines based on the DCAI (Data-Centric AI) concept.
6. Multimodal Data and Heterogeneous Computing:
Construct a multimodal data heterogeneous computing framework for recommendation systems to solve challenges in data reading, framework integration, and high-performance operator orchestration, improving data processing and model training efficiency. Establish a developer ecosystem centered on Python.
7. Large-scale computing Model Efficiency Optimization for Recommendation:
With continuous breakthroughs of large models in CV/NLP/multimodal fields and even towards AGI, large computing-driven recommendation scenarios enable models to more comprehensively and profoundly understand user preferences, thereby better interpreting user needs, excavating latent interests, and delivering superior user experiences. Larger-scale recommendation models demand greater computing. To balance computing overhead and effectiveness gains requires in-depth Co-Design by architecture and algorithm engineers.