Machine Learning Intern, E‑Commerce Knowledge Graph

TikTok; San Jose (CA); On-site; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

10 paid holidays per year

Housing allowance

Job summary

TikTok is seeking a Campus Intern to contribute to its Knowledge Graph team in California. You will help build neural network-driven data products for e-commerce, including product tagging, categorization, and category attributes, while collaborating with the recommendation team.

You will work on knowledge graphs, buyer-product connections, and research to explain why similar items outperform others. This role requires pursuing a PhD and strong ML/NLP skills, with exposure to large-scale data

Qualifications

• Currently pursuing a PhD in Software Development, Computer Science, Computer Engineering or related discipline.
• Experience in at least one of: LLMs/NLP, KG, ML, ML frameworks.
• Strong coding skills in Python/Java/C++ and familiarity with DL frameworks.

Responsibilities

• Craft knowledge graphs covering product insights and category/brand connections.
• Architect knowledge maps detailing buyer and product connections.

Skills

LLM/NLP

Natural language generation

Named entity recognition

Machine translation

Question answering

Knowledge graph

Information extraction

Text mining

Text classification

Python

Java

C/C++

Spark

PyTorch

TensorFlow

Keras

Strong analytical skills

Education

PhD in CS/CE/related

Tools

Python

Java

C/C++

Spark

PyTorch

TensorFlow

Keras

Job description

TikTok is seeking a Campus Intern to contribute to its Knowledge Graph team in California. You will help build neural network-driven data products for e-commerce, including product tagging, categorization, and category attributes, while collaborating with the recommendation team.

You will work on knowledge graphs, buyer-product connections, and research to explain why similar items outperform others. This role requires pursuing a PhD and strong ML/NLP skills, with exposure to large-scale data