Team Introduction
Our Recommendation Architecture Team is responsible for building up and optimizing the architecture for our recommendation system to provide the best experience for our TikTok users.
The team is responsible for system stability and high availability, online services and offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid-platform, realizing flexible and scalable high-performance storage and computing systems.
We are looking for talented individuals to join us for an internship in 2025.
Internships at TikTok aim to offer students industry exposure and hands-on experience.
Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply.
The application limit is applicable to TikTok and its affiliates' jobs globally.
Applications will be reviewed on a rolling basis - we encourage you to apply early.
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
- Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.
- Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg.
Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
- Design and build supporting/debugging tools as needed.
Minimum Qualifications
- Undergraduate or Postgraduate currently pursuing a Degree/Master/PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline;
- Strong programming and problem-solving ability.
- Experience in applied machine learning, familiar with common machine learning and deep learning models.
(K-means, LR, GBDT, FM, DNN, Transformer)
- Experience in Deep Learning Tools such as tensorflow/pytorch.
Preferred Qualifications
- Experience in recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
- Publications at KDD, NeurlPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related conferences/journals, or experience in data mining/machine learning competitions such as Kaggle/KDD-cup etc.