TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us
Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.

Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth.We are the Social Algorithms Team at TikTok, with a core objective of raising the long-term potential of our product through the development of social features. It has already been proven that social engagement is one of the most critical direction for our product's growth.Our 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 fast-pacing, collaborative and impact-driven.

We are looking for talented MLE engineers and research scientists to join our team in 2025, who are excited about growing their business understanding, building scalable and high-performance models and systems, and partnering across disciplines with global teams, in pursuit of excellence.

Responsibilities
1. Develop an industry-leading recommendation system aimed at enhancing the user social experience on TikTok. This system will help users discover and connect with both old and new friends, fostering a sense of community and enjoyment on the platform.
2. Deliver end-to-end machine learning solution to address critical product challenges;
3. Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.
4. Collaborate with cross-functional teams, including product managers, data scientists, and product engineers, to form and solve problems, refine machine learning algorithms, and communicate results