Backend Engineer Intern (TikTok Recommendation Architecture) - 2026 Start (PhD)
Core
Design and optimize the architecture of TikTok's recommendation system, focusing on stability, high availability, performance, and cost-efficiency for online services and offline data flows.
Role type
PhD Research Intern (Backend/Architecture)
Builds
Scalable high-performance storage and computing systems, data mid-platforms, and adaptive fault diagnosis solutions for recommendation engines.
Domain
Internet / Recommendation Systems / Large-scale Distributed Systems
Deliverable
infrastructure
Required skills
Large model capabilities, adaptive fault diagnosis, cross-domain data processing, data storage and quality enhancement, multimodal data heterogeneous computing, large-scale computing model efficiency optimization
Preferred skills
Research in NLP, computer vision, data modeling, or algorithm optimization
Technologies
Python, C/C++, DCAI (Data-Centric AI), large models (CV/NLP/multimodal)
Responsibilities
Build intelligent systems for standardized recommendation strategy definition and automatic retirement; optimize system parameters and configurations using large models; balance costs and efficiency in generative technology applications; handle massive heterogeneous data in cross-domain scenarios; develop low-cost, high-performance storage engines and data processing pipelines; construct multimodal data heterogeneous computing frameworks; optimize large-scale computing models for recommendation.
Seniority
PhD Intern
