机器学习工程师实习生 - 2027年入职

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.

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).

- Craft intricate knowledge graphs, encompassing areas like product/content insights and category/brand/SPU development;

- Architect knowledge maps detailing buyer and product connections.

- Currently pursuing a PhD in Software Development, Computer Science, Computer Engineering or a related discipline.

- 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.

- Machine learning, transfer learning, graph embedding, representative learning, large-scale optimization, recommendation, reinforcement learning, etc.

- Strong coding skills in at least one of the following programming frameworks, e.g. Python, Java, C/C++, Spark etc.;

Familiar with at least one of the following deep learning frameworks, e.g., PyTorch, TensorFlow, Keras etc.

- Familiarity with deep learning frameworks like TensorFlow/PyTorch.

- Coursework or projects related to text classification, text matching, sequence labeling, and knowledge graphs.

- Knowledge of domain adaptation, text mining, and unsupervised/semi-supervised methodologies.

- Experience with machine learning and deep learning algorithms, and text representation methods.

- Exposure to large-scale text data processing or tools like Hadoop/Spark/Hive/Flink.