Lead Machine Learning Engineer - User Growth - Recommendation - Singapore

About the Team:

The Tik Tok User Growth (UG) algorithm team is the core engine propelling the rapid growth of Tik Tok. Facing real-world challenges in manifold scenarios, the team skillfully leverages machine learning techniques on massive data to forge efficient growth pathways.
In close collaboration with product teams, we are responsible for personalized recommendation algorithms for Tik Tok's hundreds of millions of global users. We are looking for strong research scientists, who are excited about growing their business understanding, building scalable and high-performance algorithms, large models and recommendation systems.

Responsibilities:

• Develop large-scale machine learning algorithms and build industry leading recommendation systems to optimize user retention rates.
• Data-driven: connect data to real-world user experiences using superior data sensitivity and analytical skills.
• Using deep understanding of systems and scenarios, deliver end-to-end solutions to achieve the growth targets in diverse scenarios and recommendation systems.
• Work cross-functionally with product managers, data scientists, and engineers to understand insights, formulate problems, and design, refine, and test machine learning algorithms and strategies.

Requirements:

• Minimum Qualifications
• Master's degree (or higher) with a background in computer science, machine learning, or related fields.
• Experienced in leading projects and collaborating with teams across different timezones;
• Minimum 4 years hands-on experience in one or more of the following areas: recommendation systems, machine learning, deep learning, large language model, computer vision, NLP, causal inference or multimodal machine learning.
• Strong proficiency in Python and/or C/C++, and familiarity with a machine learning framework. Have a deep understanding of data structures and algorithms.
• Good communication and teamwork skills are required. Applicants must be passionate about learning new techniques and tackling challenging problems.