About the team
The Data-E-commerce Engineering team is a global team responsible for AI infra for TikTok E-Commerce ecosystem, including Search and Personization System, LLM chat bot, Risk control, Logistic and Supply Chain, Customer Experience and Governance and Knowledge Graph.
We constantly work on areas such as modeling inference and performance optimization, model training and deployment, data processing pipeline, features engineering and online services.
We are looking for talented individuals to join us for an internship in from August 2024 onwards.
Internships at TikTok aim to offer students industry exposure and hands-on experience.
Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok.
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.
Applications will be reviewed on a rolling basis - we encourage you to apply early.
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Based on the actual business situation, abstract and precipitate common business architecture, improve the reuse of basic capabilities, and better support high-speed business iteration.
- Collaborate with scientists teams to deliver high quality AI solution.
- Responsible for designing/architecting E-Commerce AI infra solution.
- Align with product and org objectives, and coordinate with cross-functional teams on delivering key results.
Minimum Qualifications
- Undergraduate, or Postgraduate who is currently pursuing a degree/master in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
- Coding experience with a general purpose programming language (ie.
Java, C/C++, Go, Python).
- Demonstrated knowledge of big data processing pipeline.
Preferred Qualifications
- Relevant experience on distributed cloud services.
- Relevant experience on AI infra (ie.
Model inference, Model training, Feature engineering, labeling system, LLM deployment).