Machine Learning Engineer Intern (Global E-Commerce, Platform Governance) - 2027 Start

Responsibilities

About the Team
The team use the most advanced AI technology to combat various risks/violations in our 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 us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.
Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.
Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.
Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

Responsibilities
1. 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.
2. 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.
3. 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.
4. Participate in building a large-scale graph storage and graph learning platform to improve relationship building and governance within the e-commerce community.
5. 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
1. Currently Pursuing a Undergraduate or Master's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
2. Solid coding skills, familiar with deep learning/graph neural network/machine learning frameworks such as PyTorch, TensorFlow, DGL, pyg, sklearn, etc.
3. 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.
4. Experience building or contributing to AI-Agent projects, including agent workflow design, tool/function calling, multi-agent collaboration, or LLM-based application development.
5. Relevant industry experience with solid theoretical foundation of machine learning.

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