Graduate ML Engineer — Graph Learning & Risk Analytics

TikTok; Singapore; On-site; Full time

Job description

Responsibilities

The team uses the most advanced AI technology to combat various risks/violations in TikTok's e-commerce platform, maintain platform security, build a good e-commerce ecosystem, and empower business teams to improve work efficiency. We pursue the ultimate risk detection capability. The fairness and sustainability of the e-commerce ecosystem, high-quality content and merchandise are areas where we are constantly striving for improvement. We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok. Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

• Responsible for using algorithms to mine, identify, and predict risks and low-quality merchants, products, and creators in e-commerce scenarios, and assist businesses in managing and controlling them.
• Responsible for using algorithms to identify events and behaviors that affect product experience and efficiency, and building data models to help create a favorable ecosystem and support business operations.
• Participate in building data for various entities such as e-commerce live streaming/video, products, merchants, and creators, building large‑scale network/feature sequence models to support business scenarios such as content understanding, multimodal representation, community mining, and solve problems related to products, live streaming, and video management, and provide support for them.
• Participate in building a large-scale graph storage and graph learning platform to improve relationship building and governance within the TikTok e-commerce community.
• Explore and research cutting‑edge technologies in machine learning/graph learning/sequence learning and related fields, implement these technologies in practical business scenarios, and support the production of scalable and optimised machine learning models.

Qualifications

Minimum Qualifications

• Final year PhD graduates in Computer Science, engineering or quantitative field.
• Solid coding skills, familiar with deep learning/graph neural network/machine learning frameworks such as PyTorch, TensorFlow, DGL, pyg, sklearn, etc.
• Familiar with one or more algorithms in machine learning/graph learning/sequence learning, such as graph modeling, timing signal modeling, node/subgraph classification, community mining, representation learning, self‑supervised/semi‑supervised learning, with a certain depth and breadth.
• Relevant industry experience with solid theoretical foundation of machine learning.

Preferred Qualifications

• Good sense of teamwork and communication skills, practical experience in relevant business scenarios is preferred.
• Published papers in high‑level computer science conferences (ACL, EMNLP, NIPS, AAAI, etc.) or have competition experience are preferred.
• Familiar with the application of related algorithms in data mining, search recommendation, content understanding, and governance risk control preferred.
• Skilled in numpy and pandas for modeling experiments, familiar with big data tools such as Hive, Spark, Hadoop preferred.

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If you have any questions, please reach out to us at [e-mail]