our Company.
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 Our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
We support credit-bearing internship registration, subject to the intern's school requirements and company approval.
Successful candidates must be able to commit to either of the following Internships
- From May to August (onboard by 11 May 2026)
Responsibilities
- Take charge of the full-process implementation of large model applications in advertising security scenarios, build an operation capability system based on llm, and drive the improvement of business efficiency and risk prevention and control capabilities.
- Explore the implementation and application of Agents across various business scenarios, and pursue the ultimate optimization of operational efficiency and quality by leveraging large model capabilities
- Collaborate with product and algorithm teams to explore innovative applications of llm in advertising security scenarios, formulate actionable strategy solutions, and promote their large-scale deployment.
Qualifications
Minimum Qualification(s):
- Undergraduate or postgraduate student currently pursuing a degree in Computer Science, Engineering, Data Science, Economics, or related fields
- Master the fundamental principles of large models (e.g., LLMs, mLLMs,Agent, etc.) and have hands-on experience.
- Proficient in Prompt Engineering (including prompt writing, optimization, and effectiveness evaluation), and familiar with Workflow/Agent design and its adaptation to business scenarios.
- Possess strong learning agility to keep pace with the latest technological developments in large models; demonstrate excellent cross-team communication and coordination skills, a high level of self-motivation and result-oriented mindset, and excel in team collaboration.
If you have any questions, please reach out to us at [email protected]