Machine Learning Engineer, NLP - TikTok E-commerce Knowledge Graph

Our team is responsible for developing state-of-the-art NLP/ML algorithms and strategies to improve user consumption experience, inspire merchants' service quality and revenue, and build a fair and flourishing ecosystem on our E-commerce Platform. More specifically, our team is responsible for the algorithms of Product Knowledge Graphs under TikTok's global e-commerce business.

What you will do:

• Participate in the development of massive knowledge graphs of real-world products to support feed ranking, recommendations, and ads.
• Collaborate with product managers, data scientists, and the product strategy & operation team to define product strategies and features.

Responsibilities:

• Knowledge graph construction, including product/content/feedback understanding and category/brand/SPU construction.
• Construct knowledge graphs of buyers and products.Minimum Qualifications:
• Bachelor's degree in Computer Science or related technical field
• 3+ working experience in one of the following fields: machine learning, NLP, and computer vision
• Experience with software development in at least one of the following programming languages: C++, Python, Go, Java
• Good sense of teamwork and communication skills, practical experience in relevant business scenarios is preferred.

Preferred Qualifications:

• Proficient in using at least one mainstream deep learning frameworks such as TensorFlow/PyTorch, understanding distributed training, distillation acceleration, and other implementation methods.
• Experience in text classification, text matching, sequence labeling, knowledge graph.
• Aware of certain processing methods and optimization experience on domain adaptation, small sample construction, text mining, unsupervised/semi-supervised and other similar issues.
• Familiar with commonly used machine learning and deep learning algorithms, understand basic network model structure (DNN/LSTM/CNN, etc.) and text representation methods (LDA/Word2Vec/ELMo/GPT/BERT, etc.), have practical experience in deep learning training and reasoning model tuning.
• Experience in large-scale text data processing or cleaning (Such as using Hadoop/Spark/Hive/Flink).