About the job
We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Responsibilities
Build industry-leading recommendation system, improving user experience, content ecosystem and platform security;
Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new capabilities in content discovery.
Build multi-model and cross-scenario systems enabling unified recommendation across livestreams, short videos, and search.
Deliver end-to-end machine learning solution to address critical product challenges;
Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.
Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets.
Qualifications
Minimum
Currently pursuing PhD in Computer Science, Computer Engineering, or a related technical discipline.
Strong foundation in machine learning, with knowledge of cutting-edge AI technologies; publications in top-tier academic conferences or competition experience are preferred.
Familiarity with big data frameworks such as Hadoop, MapReduce, and Spark.
Experience with TensorFlow or PyTorch for model training and deployment; understanding of training acceleration techniques such as mixed precision and distributed training.
Preferred
Knowledge of model compression and inference acceleration techniques, including but not limited to quantization, pruning, distillation, and TensorRT optimization.
Expertise in at least one of the following areas:
- Computer Vision & Multimodality: In-depth research experience in multimedia or computer vision fields, including but not limited to image search, image/video classification and recognition, image segmentation, object detection, OCR, graph neural networks, multimodal learning, and unsupervised/self-supervised learning. Experience with large-scale CV/multimodal models, particularly in e-commerce scenarios, including developing and optimizing multimodal models for e-commerce videos and products. Ability to integrate LLMs with video/product representation