Infrastructure Engineer Intern (TikTok Recommendation Architecture) - 2027 Start (PhD)

Our Team is responsible for the design and development of the Recommendation and Search system architecture for TikTok. 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.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date). Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities - Conduct novel research on distributed training and inference system optimization for large-scale recommendation models and Large Language Models - Design and implement high-performance GPU kernel architectures and communication primitives to accelerate deep learning workloads - Publish original research findings at top-tier academic conferences and collaborate with cross-functional teams to translate research into production impact

Minimum Qualifications: - Currently pursuing a PhD in Computer Science, engineering or quantitative field - Strong programming skills in C++/CUDA/Python, with solid understanding of deep learning frameworks (PyTorch/TensorFlow) - Published research in systems, machine learning, or related areas at venues including SOSP, OSDI, NSDI, ICML, NeurIPS, ICLR, or equivalent conferences

Preferred Qualifications: - Hands-on experience with distributed training frameworks (DeepSpeed, FSDP, Megatron-LM) or GPU kernel optimization - Experience with LLM inference/serving systems such as vLLM, TensorRT-LLM, or SGLang - Strong background in parallel computing, computer architecture, or machine learning systems

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