Machine Learning Engineer Intern (E-commerce-Intelligent Customer Service) - 2025 Summer/Fall (MS)

ble for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks beginning in May/June 2025 or August/September 2025 (Select May if Summer or August if Fall
Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:
Monday, May 12
Monday, May 19
Tuesday May 27 (Memorial Day May 26)
Monday, June 9
Monday, June 23

Fall Start Dates:
Monday, August 11
Monday, August 25
Monday, September 8
Monday, September 22

Applications will be reviewed on a rolling basis. We encourage you to apply early. Candidates can apply to a maximum of TWO positions and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally.

Candidates who pass resume evaluation will be invited to participate in TikTok's technical online assessment through HackerRank.

Responsibilities:

• Applying LLMs for a variety of multilingual and multimodal scenarios of e-commerce business;
• Establishing a generative-based AI chatbot that is capable of resolving post-sale issues and achieving pre-sale interactive consultations;
• Text generation, including product titles and descriptions, dialogue summary, auto-replying of emails and tickets, etc.;
• Machine translation, including optimizing translation quality in dozens of scenarios and language pairs in the e-commerce scenarios, as well as the research of related cutting-edge technologies.

Qualifications

Minimum Qualifications:

• Currently pursuing a Master's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline;
• 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:

• Experience in NLP and LLM technologies;
• Proficient in using at least one mainstream deep learning framework such as TensorFlow/PyTorch, understanding distributed training, distillation acceleration, and other implementation methods;
• 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)