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.

About the Team
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 fast-pacing, collaborative and impact-driven.

We are looking for talented individuals to join our team in 2024. 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.

Responsibilities:
- Build industry-leading recommendation system, improving user experience, content ecosystem and platform security;
- Deliver end-to-end machine learning solution to address critical product challenges;
- Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.
- Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets.

Location

San Jose, California, United States

Job Overview
Job Posted:
1 day ago
Job Expires:
Job Type
Full Time

Share This Job: