E-commerce Recommendation Systems Intern

4 weeks ago

Sparta, Michigan, United States Tik Tok Full time


Team Overview

Join our dynamic Recommendation Architecture Team, dedicated to enhancing and refining the framework for our recommendation system, ensuring an exceptional experience for TikTok users.

This team plays a crucial role in maintaining system reliability and availability, optimizing online services, and improving offline data flow performance. We tackle system limitations, minimize cost inefficiencies, and construct a robust data and service mid-platform, enabling the development of flexible and scalable high-performance storage and computing systems.

We are on the lookout for enthusiastic individuals to embark on an internship journey. Our internships at TikTok are designed to provide students with invaluable industry exposure and practical experience. Here, your aspirations can transform into reality, unlocking endless opportunities.

Applicants may submit their applications for up to two positions, which will be evaluated in the order received. This application limit applies to TikTok and its affiliates globally. Applications will be assessed on a rolling basis, and we encourage early submissions. Successful candidates should be prepared for a minimum commitment of three months.


Key Responsibilities


• Engage in the development of large-scale e-commerce recommendation algorithms and systems, covering areas such as product recommendations, live stream suggestions, and short video recommendations.


• Construct both long-term and short-term user interest models, sifting through extensive datasets to extract pertinent information and design algorithms that effectively uncover users' hidden interests.


• Design, develop, assess, and refine predictive models for candidate generation and ranking, including but not limited to real-time data pipelines, feature engineering, and model optimization.


• Create and enhance supporting and debugging tools as necessary.


Minimum Qualifications

• Currently enrolled in an Undergraduate or Postgraduate program pursuing a Degree, Master’s, or PhD in Software Development, Computer Science, Computer Engineering, or a related technical field.

• Strong programming skills and problem-solving capabilities.


• Experience in applied machine learning, with familiarity in common machine learning and deep learning models such as K-means, Logistic Regression, GBDT, Factorization Machines, Deep Neural Networks, and Transformers.


• Proficiency in Deep Learning frameworks such as TensorFlow or PyTorch.


Preferred Qualifications

• Background in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related domains.


• Publications in conferences or journals such as KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS, or participation in data mining/machine learning competitions like Kaggle or KDD-cup.

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