Tech Lead Machine Learning Engineer - TikTok Content Ecology

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.

TikTok Content Ecology Algorithm Team leverages multimodal, LLM/MLLM, NLP & CV, and recommendation technologies to provide platform-level algorithmic capabilities for various business lines, including local services, search, main architecture, PGC (Professionally Generated Content), and UG (User Growth).
Our work spans short video content understanding, global trending event detection, intelligent customer service, and inspiration recommendation.
There are over a hundred languages here, presenting multilingual challenges for NLP/LLM technologies.
With a vast amount of short video content, MLLM and multimodal technologies have more application scenarios. The platform has a large user base, with hundreds of millions of creators, providing rich application scenarios for inspiration recommendation technologies.
All business lines are in a period of rapid growth, with overall DAU and revenue increasing rapidly, offering vast development opportunities.
Welcome to join us！

Responsibilities:
- As the Tech Lead, responsible for leading the implementation of content ecology algorithm projects across various business lines.
- Ensuring optimal technology selection, and considering NLP, CV, multimodal, LLM, search and recommendation technologies for different projects.
- Collaborate and communicate with multiple cross-functional teams to drive project progress.
- Consider localization deployment solutions and maintain close collaboration with local teams.