Machine Learning Engineer Intern (TikTok BRIC Singapore) - 2025 Start (BS/MS)

The Business Risk Integrated Control (BRIC) team of TikTok, is missioned to:
- Protect TikTok users, including and beyond content consumers, creators, advertisers.
- Secure platform health and authenticity of community experience .
- Collaborate with cross-functional stakeholders to improve TikTok infrastructures, services, tools and algorithms, towards a higher standard of privacy and security.

The BRIC team works to measure and minimize the damage implied by inauthentic behaviors on TikTok and its extended platforms, covering multiple classical and novel integrity/security areas such as fake accounts, fake traffic, spam, scraping, cyberbullying, live room risks, incentive fraud, monetization abuse, etc.

In this team, you will have a unique opportunity to:
- Have first-hand experience contributing to TikTok's key security initiatives.
- Build scalable, resilient, intelligent integrity solutions that are also privacy-safe and user-friendly.
- Solve product and technical challenges that are often first-seen and highly adversarial. Both quick short term tactics and long term strategical moves are expected at various paces.
- Be part of a diverse and enthusiastic global workforce, grow with and learn from security and integrity experts across Asia, Europe, North America, etc.

As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests.

Applications will be reviewed on a rolling basis - we encourage you to apply early.

Successful candidates must be able to commit to at least 3 months long internship period.

Job Responsibilities:
- Build machine learning solutions to respond to and mitigate business risks in ByteDance products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc.
- Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups.
- Level up risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis.