LLM Research Scientist Graduate (eCommerce Recommendation)- 2025 Start (MS) job opportunity at Tiktok.

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Tiktok LLM Research Scientist Graduate (eCommerce Recommendation)- 2025 Start (MS)
Experience: 1-year
Pattern: full-time
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Research Scientist

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degreeMaster's (M.Sc.)
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

Responsibilities #E-commerce is a new and 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 about applying large scale machine learning to solve various real-world problems in E-commerce. The rapid development of large language models has brought more possibilities to e-commerce business. We are actively trying to use large language models to solve some problems in e-commerce recommendations and have made substantial progress. Therefore, we will continuously invest more resources in the research of recommendation algorithms based on large language models. We are looking for talented individuals to join our team in 2025. 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 TikTok. Responsibilities: 1. Continuously track and understand the latest research progress of large language models, including pre-training, fine-tuning, instruction fine-tuning, prompt engineering, multimodal large models, etc. 2. Actively explore the combined application of large language models and recommendation systems to solve practical problems in recommendation systems, including person-goods matching, commodity governance, interest exploration, commodity understanding, and generative recommendation. 3. Cooperate with the infrastructure team to improve the efficiency of large model training, develop large model services, and improve the stability and scalability of training and services. 4. Cooperate with scientists in the team to #publish #research results in top academic conferences and expand the influence of the team

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