Machine Learning Engineer Graduate (E-Commerce Risk Control - USDS) - 2025 Start (MS)

About the team
The E-Commerce Risk Control team works to minimize the damage of inauthentic behaviors on Tiktok E-Commerce platforms, covering multiple classical and novel business risk areas such as account integrity, incentive abuse, malicious behaviors, brushing, click-farm, information leakage, etc.

In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolution of a phenomenal product eco-system. The work needs to be fast, transferable, while still down to the ground to make quick and solid differences.

We are looking for talented individuals to join our team in 2025. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok USDS.
Please state your graduation date clearly in your resume.
Applications will be reviewed on a rolling basis. We encourage you to apply early.

Responsibilities:
- Build machine learning solutions to respond to and mitigate business risks in Tiktok products/platforms. Such risks include and are not limited to abusive account integrity, scalper, deal-hunter, malicious activities, brushing, click-farm, information leakage 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.
- Up-level risk machine learning excellence in privacy/compliance, interpretability, risk perception and analysis.
- Build fraud detection, anomaly detection, and risk-scoring models using supervised, unsupervised, and deep learning techniques.
- Apply graph-based models for detecting fraud networks

In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.