Machine Learning Engineer Graduate (TikTok Recommendation-Live) - 2026 Start (BS/MS)
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About this role
The recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is dynamic, fast-pacing, collaborative and impact-driven.
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
- Bachelor/Master degree in computer science, computer engineering, information systems or a related technical discipline from a university
- 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