Machine Learning Engineer Graduate (Recommendation) - 2026 Start (PhD)

About the TikTok Data TeamOur TikTok team is responsible for developing state-of-the-art machine learning models and strategies to improve user consumption experience, inspire creativity, build a fair and flourishing ecosystem.

We are looking for talented individuals to join our team in 2026.

As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Launch your career where inspiration is infinite at TikTok Successful candidates must be able to commit to an onboarding date by end of year 2026.

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 TikTok and its affiliates' jobs globally.

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

Responsibilities
- Build industry leading recommendation system; develop highly scalable classifiers and tools leveraging machine learning;
- Understand product objectives and machine learning techniques; improve model and recommendation strategy;
- Understand user behavior and apply ML algorithms to optimize content consumption and production experience;
- Understand content security strategy and apply ML algorithms to improve content audit process;
- Work with cross functional teams to grow company's product in important regional markets.



Minimum Qualifications:
- Currently pursuing a PhD in Computer Science or a related technical discipline.
- Solid experience with data structures or algorithms
- Software development experience through hands on coding in a general purpose programming language
- Strong communication and teamwork skills
- Passion about technologies and solving challenging problems Preferred Qualification:
- Experience in one or more of the following areas: machine learning, recommendation systems, data mining or other related areas By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https:///legal/privacy
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