Applied Scientist Intern - Business Integrity - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

cation to technical excellence. We aim to meet our users' needs with reliable and high-performing platforms and services. We are looking for strong machine learning engineers who are excited to grow their business understanding, build highly scalable machine learning models, and partner across disciplines with global teams, in pursuit of excellence. Given the fast growth of TikTok in the world, we are working on building a next-generation content understanding system for TikTok monetization. We are seeking Research Engineers who are experienced in machine learning, which can help us create an ecosystem that rewards high-quality user experience and advertiser value.

Topic Content: With the explosive growth of digital content, intelligent moderation has become a core capability for internet platforms. However, as moderation scenarios grow increasingly complex and adversarial tactics continue to evolve, traditional approaches are facing unprecedented challenges. The current landscape is characterized by multiple technical difficulties, including the dynamic nature of moderation rules, content complexity, sample scarcity, escalating adversarial behaviors, and a lack of interpretability. In particular, existing open-source large models often do not perform as effective as we expect in scenarios involving evolving moderation rules, long-form text, long temporal sequences, multi-languages, limited sample data, and adversarial content generated by AIGC.

Responsibilities:
In this role, you will build a leading moderation system that enables end-to-end capabilities for accurate rejection decisions, interpretable reasoning, and intelligent remediation, achieving fully automated moderation with performance surpassing human benchmarks.

Qualifications

Minimum Qualifications:

1. Currently pursuing a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
2. Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV, multimodal, agent technologies.
3. Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and
   practical experience in areas such as single-modal LLM application and deployment.
4. Strong publications record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, N
   CVPR, ICCV, and ECCV)