Machine Learning Engineer Intern (E-Commerce Knowledge Graph)- 2027 Summer

Responsibilities

Team Introduction:
The Knowledge Graph team is responsible for the neural network of e-commerce, which includes tasks such as categorizing products, influencers, and merchants, mining product information, and providing this data to the recommendation team. Additionally, they manage operations related to product tags and category attributes. When we come across a product, they determine whether it belongs to the clothing or sports category, who thie product will be suitable for, etc. Furthermore, the team conducts competitive research, identifying top-performing items on the platform and analyzing why one similar product outperforms others. They are also involved in brand recognition. This team plays a pivotal role, in bridging various aspects of our business and maintaining close collaboration with other teams.

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:
- Craft intricate knowledge graphs, encompassing areas like product/content insights and category/brand/SPU development;
- Architect knowledge maps detailing buyer and product connections.

Qualifications

Minimum Qualifications:
- Currently pursuing a Undergraduate in Software Development, Computer Science, Computer Engineering or a related discipline.
- Experience in at least one of the technological areas:
- LLM, natural language generation, named entity recognition, machine translation, question answering, knowledge graph, information extraction, text mining and classification etc.
- Computer vision, facial recognition, multimodal learning, object detection & tracking, image classification etc.