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

About the role

The algorithm team develops state-of-the-art computer vision, NLP, and multimodality models to protect TikTok’s platform and users from content and behaviors that violate community guidelines and regulations. This role offers the opportunity to work on cutting-edge content understanding models, improve violation detection, and optimize distributed model training frameworks.

Responsibilities

• Develop computer vision or multimodality models to recognize violating content on TikTok.
• Explore cutting-edge multimodal or computer vision large models (e.g., CLIP, COCA, ALBEF, BLIP, Flamingo, ViT-G, ViT-22B, EVA-enormous).
• Apply large language models (LLM) to business scenarios, including pre-training, zero-shot/few-shot learning, and hard case mining.
• Optimize the training framework to better support large-scale model training.

Requirements

• Completion or recent completion of a PhD in Computer Science or a related discipline.
• Research experience in at least one of the following: computer vision, multimodality, or large language models (LLM).
• Proficiency in at least one deep learning framework (e.g., PyTorch, TensorFlow).
• Strong analytical, problem-solving, logical thinking, communication, and collaboration skills.

Preferred Qualifications

• Published papers in accredited academic conferences or journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV).

Pay

The base salary range for this position is $162,000 – $316,800 annually, with variations based on qualifications, skills, experience, and location. Additional compensation may include discretionary bonuses, incentives, and restricted stock units.

Benefits

• Day-one access to medical, dental, and vision insurance.
• 401(k) savings plan with company match.
• Paid parental leave, short-term and long-term disability coverage, and life insurance.
• Wellbeing benefits.
• 10 paid holidays, 10 paid sick days, and 17 days of Paid Personal Time per year (prorated upon hire with accruals increasing by tenure).