Machine Learning Engineer Graduate (E-Commerce Risk Control)- 2027 Start (PhD)

Our team is building the next generation of AI-native risk intelligence systems to address emerging challenges driven by large-scale AIGC content production.

As content creation becomes automated and adversaries adopt systematic experimentation (e.g., large-scale template variation and rapid iteration), traditional rule-based and single-model approaches are no longer sufficient. We are transitioning from monolithic LLM applications to a structured multi-agent architecture that emphasizes:

• Tool-augmented reasoning (ReAct-style systems)
• Modular skill composition
• Execution traceability and observability
• Feedback-driven system evolution
• Cross-domain risk reasoning

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Develop and implement innovative machine learning algorithms to manage business risks in TikTok's products and platforms.
• Prototype and explore novel solutions, conduct experiments to validate hypotheses, and provide insights to Product and Tech teams.
• Collaborate with multidisciplinary teams to enhance current automation processes.
• Build efficient data querying infrastructure for real-time and offline analysis. Identify opportunities to strengthen risk defense solutions.
• Define risk control metrics and promote data-driven practices. Align teams and stakeholders on numeric goals for risk control.
• Document technical processes and effectively communicate results through writing, visualizations, and presentations.Minimum Qualifications:
• Individuals who are completing or have recently completed a PhD degree in Computer Science, Computer Engineering or a related discipline.
• Proficiency in modern machine learning theories and applications, including ensemble trees, deep neural networks, transfer/multi-task learning, reinforcement learning, graph theory, and unsupervised learning.
• Experience with Python, SQL/Hive, and Hadoop
• Strong understanding of data structures and algorithms, with excellent problem-solving ability
• Detail oriented, effective time management, and strong analytical skills

Preferred Qualifications:

• Demonstrated software engineering experience from previous internship, coding competitions, or publications
• Internship experience or research experience, especially in e-commerce, risk control domain
• Curiosity towards new technologies and entrepreneurship
• High levels of creativity and quick problem-solving capabilities