Research Scientist Graduate (eCommerce Recommendation)
3 Months ago • All levels • $137,750 PA - $237,500 PA
Research Development
Job Description
The E-commerce Recommendation Team is seeking a Research Scientist Graduate to join their team in 2026. The successful candidate will collaborate on cutting-edge research in machine learning algorithms, including retrieval and recommendation algorithms, and participate in building large-scale e-commerce recommendation systems for TikTok. Responsibilities also include building user interest models, analyzing data, designing algorithms, and applying machine learning to improve search ranking, natural language and video understanding, and trust and safety. Successful candidates must be able to commit to an onboarding date by the end of 2026.
Apply machine learning to improve business scenarios.
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Team Introduction: Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users. We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with ByteDance Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities: • Work in a team to conduct cutting-edge research in machine learning algorithms, such as retrieval and recommendation algorithms • 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 • Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safety
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