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.

We are a pioneering Machine Learning Engineering team at TikTok, committed to spearheading innovation in leveraging cutting-edge technologies in foundation models (eg. Large Language Models, LLM) to enhance TikTok's experience. The mission of our team is to bridge the gap between state-of-the-art AI techniques and seamless user experience. Our team culture cherishes the "First Principles Thinking" approach, and we deeply value understanding things at their core and focusing on delivering solid, well-grounded solutions.

We are looking for talented 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. Deploy, prompt, and optimize cutting-edge foundation models (eg. Large Language Models, LLM).
2. Apply foundation models to enhance and optimize TikTok's recommendation system and product offerings, improving the experience of billion-scale consumers and creators.
3. 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.
4. Regularly run A/B tests, perform analyses, and iterate algorithms based on results.
5. Work with infrastructure teams on improving the efficiency and stability of machine learning systems.