Research Scientist Graduate (TikTok Trust and Safety) - 2027 Start (PhD)

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 regulations. With the continuous efforts from our team, Tik Tok is able to provide the best user experience and bring joy to everyone in the world.

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 Tik Tok, and will also be responsible for optimizing our distributed model training framework continuously.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Develop computer vision model or multimodality model to recognize violation content in Tik Tok
• Explore cutting-edge multimodal or computer vision large models (CLIP, COCA, ALBEF, BLIP, Flamingo, ViT-G, ViT-22B, EVA-enormous, etc)
• Explore the application of LLM in our business scenarios, like pre-training, zero-shot/ few-shot learning, hard case mining, etc
• Continuously optimize the training framework to better adapt to the training of large models

Requirements:

Minimum Qualifications:

• Individuals who are completing or have recently completed a PhD degree in Computer Science or a related discipline.
• Related Research Experience at least one of the following areas: computer vision, multimodality, LLM
• Be proficient with at least one deep learning framework (e.g. Py Torch, Tensor Flow)
• Have excellent analytical and problem-solving skills, logical thinking skills, communication and collaboration skills

Preferred Qualifications:

• Published papers in the accredited academic conferences or journals is a plus, including CVPR, ICCV, ECCV, NIPS, ICML, ICLR, TPAMI, IJCV, etc