About TikTok
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 we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible.
Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company.
Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come.
By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users.
When we create and grow together, the possibilities are limitless.
Join us.

About the Team
Our team plays a crucial role in ensuring the company’s success. We seek people who are willing to learn and put in the effort to solve problems. Our challenges are not your regular day-to-day problems - you’ll be part of a team that’s developing new solutions to new challenges. It’s working fast at scale, and we’re making a difference. We are looking for talents to join us on this exciting journey!

Responsibilities
Work across the full machine learning (ML) modeling lifecycle, to develop, build, and optimize the performance of a large-scale recommendation system.
Build and train ML, deep learning models and their variants to analyze and predict user behavior metrics and treatment effects with ML frameworks.
Optimize the core algorithms and strategies (recall, coarse ranking, fine ranking, mixed ranking, and diversity) through modeling technologies including deep learning, representation learning, multi-task learning, causal inference, and sequence modeling.
Continuously improve recommendation technology by applying a deep understanding of the ecological roles of users, creators, platforms, drive healthy growth in user experience, creator growth, platform revenue, and create a virtuous cycle of livestream ecosystem.
Collaborate with product and operations teams and excel in technological innovations to achieve business development goals.
Mentor junior Machine Learning Engineers and interns.