Our Recommendation Architecture Team is responsible for building up and optimizing the architecture for our recommendation products to provide the most stable and best experience for our TikTok users. University graduates are important parts to our team with your fresh ideas and creative thoughts.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.

Successful candidates must be able to commit to an onboarding date by end of year 2026.

We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to TikTok and its affiliates' jobs globally.

Responsibilities - What You'll Do

• Build and maintain high performance online services for TikTok recommendation system to support various types of products, such as For You Feed, E-commerce, Social, etc.
• Build extremely efficient and reliable data pipelines for candidates generation, profile generation, training examples generation, realtime online training, etc;
• Build globalized large-scale recommendation system;
• Design and develop high performance computing frameworks and storage systems.

Qualifications

Minimum Qualifications:
• PhD's degree (or expected by 2026) in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
• Research background in ML/AI, with a focus on inference optimization, model acceleration, or efficient ML systems.
• Experience in programming, included but not limited to, the following programming languages: C, C++, Java or Python.
• Problem-solving skills, self-driven learning mindset, and effective communication.

Preferred Qualifications:

• Demonstrated ability to translate research into practical system implementations.
• Experience (academic or practical) in areas such as recommendation systems, search, distributed systems, or big data processing.