About this role
The Brand Ads Team builds technologies that unlock business growth potential. This team owns several ads products: reservation ads, auction ads, and innovative content ads that enables advertisers and users to foster more awareness of their brand to attain their business goals. We work on the end-to-end ads delivery tech stack, including ads bidding, ranking, and forecasting.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
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.
Responsibilities:
- Build and optimize the delivery stack across pacing, allocation, traffic strategy, and delivery controls.
- Drive products from concept to launch with Product, Strategy, and cross-functional partners.
- Turn business goals into scalable delivery strategies and system designs.
- Own technical design, implementation, experimentation, launch, and iteration.
- Use data and experiments to diagnose problems and drive fast iteration.
Qualifications
Minimum Qualifications:
- Individuals who are completing or have recently completed a Bachelor's degree in Computer Science or a related discipline.
- Strong coding, debugging, and system design fundamentals.
- Proficiency in Go, C/C++, Java, Python, or similar languages.
- Comfort with ambiguity and the judgment to make clear engineering trade-offs.
- Strong cross-functional communication and collaboration skills.
- Experience with large-scale backend, recommendation, or advertising systems.
Preferred Qualifications:
- Hands-on ads delivery experience in pacing, allocation, forecasting, traffic strategy, or guaranteed delivery, and familiarity with reservation or guaranteed product models.
- Experience applying LLMs or agent systems in production — prompt engineering, RAG, fine-tuning, tool-use/agent orchestration (e.g., MCP), or LLM-assisted engineering workflows
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