fast growing business that aims at connecting all customers to excellent sellers and quality products on TikTok Shop, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. We are a group of applied machine learning engineers and data scientists that focus on E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems in E-commerce.
We are looking for talented individuals to join us for an internship in 2024. Internships at TikTok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at TikTok.
Internships at TikTok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. This Internship Program runs for 12 weeks beginning in May/June 2024. Successful candidates must be able to commit to one of the following summer internship cycles below:
1.May 20, 2024 to August 9, 2024
2.May 27, 2024 to August 16, 2024
3.June 10, 2024 to August 30, 2024
We will prioritize candidates who are able to commit to these start dates. Please state your availability clearly in your resume (Start date, End date).
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.
• 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
• Bachelor's degree in Computer Science or related technical field
• 2+ years of working experience in one of the following fields: recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
• Familiar with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep etc.
• Experience in Deep Learning Tools such as tensorflow/pytorch
• Experience with at least one programming language like C++/Python or equivalent
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at [email protected].