Research Scientist-CV/NLP/Multimodal LLM Graduate (Trust and Safety Platform)- 2026 Start (PhD) job opportunity at Tiktok.

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Tiktok Research Scientist-CV/NLP/Multimodal LLM Graduate (Trust and Safety Platform)- 2026 Start (PhD)
Experience: 1-year
Pattern: full-time
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Research

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degreePhD
Singapore

The algorithm team is responsible for developing state-of-the-art computer vision, NLP and multimodality models and algorithms to protect our platform and users from the content and behaviors that violate community guidelines and related local regulations. With the continuous efforts from our team, TikTok is able to provide the best user experience and bring joy to everyone in the world. We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok 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. Job Description We are looking for a highly self-motivated research engineer to join our algo team. In our team, you will have the opportunity to participate in the development of the cutting-edge content understanding model to help improve the recognition ability of violated content in TikTok, and will also be responsible for optimizing our distributed model training framework continuously. The ideal candidate should be experienced in building a computer vision model or multimodality model and have a very strong coding ability in Pytorch/Tensorflow Responsibilities 1. Develop computer vision model or multimodality model to recognize violation content in TikTok 2. Explore cutting-edge multimodal or computer vision large models (CLIP, COCA, ALBEF, BLIP, Flamingo, ViT-G, ViT-22B, EVA-enormous, etc) 3. Explore the application of LLM in our business scenarios, like pre-training, zero-shot/ few-shot learning, hard case mining, etc 4. Continuously optimize the training framework to better adapt to the training of large models

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