Machine Learning Engineer (Anti Automation) - TikTok BRIC (Singapore)

Responsibilities

Anti-Automation Solutions (AA) is part of Business Integrated Risk Control(BRIC) team. Automated fraud refers to technical methods used by hackers to perform API abuse at scale using scripts and tools. Our vision is to establish an industry-leading team with a focus on end-to-end defense-in-depth systems with privacy and business needs in mind. We are responsible for continuously iterating over identifying and combating automated fraud through client environment inspection, risky signals collection, trusted computing, traffic validation, data mining and producing a wide variety of tools and subject matter services for business lines.

Job Description
- Build machine learning solutions to respond to and mitigate business risks in TikTok 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.
- Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis.

Qualifications

Minimum Qualifications:
- Master or above degree in computer science, statistics, or other relevant, machine-learning-heavy majors.
- Solid engineering skills. Proficiency in at least two of them: Linux, Hadoop, Hive, Spark, Storm.
- Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning.

Preferred Qualifications:
- 3 years and above of industry experience in a software development environment.
- Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.