AI Infrastructure Optimization Engineer Intern (TikTok Global E-Commerce Recommendation & Search Architecture) - 2027 Start

Description

E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. Our E-commerce Recommendation Infra team is responsible for building up and optimizing the infrastructure for such recommendation systems, so as to provide the best experience for our users. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques. With the rapid development of LLM technologies, traditional deep learning algorithms and system architectures for recommendation are facing both transformational challenges and opportunities, including lifelong behavior modeling, model scale-up, and end-to-end generative recommendation.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.
Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.
Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.
Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.
Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities
- Research and implement LLM-based recommendation foundation model training and inference optimization technologies for e-commerce scenarios
- Build model optimization toolchains including quantization, pruning, and distillation for deep learning frameworks
- Design and implement distributed inference serving architectures supporting large-scale LLM deployment for recommendation use cases

Requirements

Minimum Qualifications：
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline
- Solid programming skills in C++/CUDA/Python, with basic understanding of GPU architecture and distributed training
- Familiarity with deep learning frameworks (PyTorch, TensorFlow) and basic knowledge of LLMs

Preferred Qualifications:
- Hands-on experience with LLM inference acceleration technologies (model quantization, KV cache optimization, FlashAttention)
- Practical experience using LLM systems such as Megatron-LM, DeepSpeed, vLLM, or TensorRT-LLM
- Interest in recommendation systems and generative AI technologies