Machine Learning Engineer - Intern (TikTok BRIC) - 2027 Start (PhD)

Responsibilities

"Business Risk Integrated Control (BRIC) is a global team protecting ByteDance users, platform health, and the authenticity of community and business ecosystems. We build the risk control algorithms, AI technologies, platforms, and infrastructure behind that work, across products including TikTok, CapCut, Lemon8, and Lark.

Our work spans account and content integrity, and safety across advertising and e-commerce. We apply machine learning across areas such as graph learning, anomaly detection, and multimodal models to understand users, content, behavior, and the relationships between them. The challenge is recognizing genuine activity accurately, so the people using our products never notice the systems working.

You will shape the trust, safety, and sustainable growth of ByteDance's global platforms, applying advanced AI to some of the hardest real-world problems in the field."

Program Intern (Conversion-Oriented): for graduating students who are looking for full-time conversion as the primary objective.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

Responsibilities:
- Leverage algorithms and cutting-edge AI technologies & tools to tackle account security risks across TikTok and other overseas offline businesses, including social, live streaming, local services, e-commerce, payment and other scenarios.
- Address account security issues including but not limited to bulk fake account, account trading, malicious farming, account takeover, ticket hijacking, etc.
- Build features and representation learning based on multi-source data (account behavior, ecosystem behavior, device, graph relationships, text & image content, etc.). Improve real-time and offline risk perception and detection capabilities of models. Conduct model evaluation and online experiments, with close attention to stability, drift monitoring and model interpretability.