Tech Lead, Senior Machine Learning Engineer - TikTok Search Algorithms (NLP, Ranking, Relevance, Understanding, User Engagement)

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 Role

TikTok is seeking a visionary and technically adept leader to head our Personalized Search team. This role is pivotal in advancing TikTok's search capabilities by integrating cutting-edge recommendation algorithms and Large Language Model (LLM) technologies to deliver highly personalized and engaging user experiences.



Responsibilities

- Technical Leadership: Lead the design, development, and deployment of large-scale personalized search and recommendation systems, ensuring scalability, efficiency, and robustness.

- Innovation with Generative Recommendation Systems: Drive the integration of generative recommendation approaches, leveraging LLMs to directly generate personalized content recommendations, moving beyond traditional ranking-based methods. This includes exploring frameworks like GenRec, which utilize LLMs to interpret user contexts and generate relevant recommendations.

- LLM Integration: Implement and fine-tune LLMs within the recommendation pipeline to enhance content understanding, user intent recognition, and personalization. Explore hybrid models that combine LLMs with traditional recommendation systems to mitigate feedback loops and uncover novel user interests.

- Research and Development: Stay abreast of the latest advancements in machine learning, NLP, and recommendation systems, applying relevant findings to improve TikTok's search experience.

- Cross-functional Collaboration: Work closely with product managers, data scientists, and infrastructure engineers to align search personalization strategies with overall product goals.

- Team Development: Mentor and grow a team of engineers and researchers, fostering a culture of innovation, collaboration, and continuous learning.