Machine Learning Engineer Intern (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 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:

• 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:

• Currently pursuing a PhD in Software Development, 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: