Applied Scientist Intern - Business Integrity - Global Frontier Tech Program

We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands‑on learning, enriching community‑building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Team Introduction:
Our Business Integrity team has a strong user focus and a dedication 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 Tik Tok in the world, we are working on building a next‑generation content understanding system for Tik Tok 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.

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.