PhD ML Intern: Graph & Risk Analytics for E-commerce

TikTok; Singapore; On-site; SGD 24,000 - 42,000; Full time

Job summary

TikTok is inviting PhD interns to contribute to AI research and product development in Singapore. You will apply ML/graph learning to detect risks, improve content quality, and support business operations on a world-leading short-video platform.

The internship blends hands-on learning, community-building, and collaboration with industry experts, with opportunities to work on live systems and scalable ML models.

Qualifications

• Pursuing a PhD in Software Development, CS, CE, or related technical field.
• Strong coding skills; familiar with PyTorch, TensorFlow, DGL, pyg, sklearn.
• Familiar with ML/graph/sequence learning algorithms with depth and breadth.
• Experience building/contributing to AI-Agent projects including tool calling or multi-agent collaboration.
• Industry exposure with solid ML theory foundations.

Responsibilities

• Mine, identify, and predict risks and low-quality merchants, products, and creators in e-commerce; assist business controls.
• Identify events affecting product experience; build data models to support ecosystem and operations.
• Build data for entities like live streaming, products, merchants, and creators; develop large-scale graphs and models for content understanding and governance.
• Develop a large-scale graph storage and graph learning platform to improve relationship building and governance.
• Explore cutting-edge ML/graph/sequence tech and implement in production-scale models.

Skills

PyTorch

TensorFlow

DGL

pyg

scikit-learn

Graph learning

Machine learning

Deep learning

Education

PhD in Computer Science/Engineering

Tools

Hive

Spark

Hadoop

Job description

TikTok is inviting PhD interns to contribute to AI research and product development in Singapore. You will apply ML/graph learning to detect risks, improve content quality, and support business operations on a world-leading short-video platform.

The internship blends hands-on learning, community-building, and collaboration with industry experts, with opportunities to work on live systems and scalable ML models.