Responsibilities:

1. Design, develop, and optimize algorithms for core recommendation scenarios , covering key stages such as cold start, recall, ranking, and ecosystem strategy;

2. Research and implement state-of-the-art machine learning, deep learning, and large language models (LLMs) to continuously enhance recommendation accuracy and user experience;

3. Perform deep data analysis and modeling on large-scale datasets to support ecosystem health, user growth, and long-term retention in the live-streaming community.

Job Requirements:

1. Strong programming skills with knowledge of data structures and algorithms; commitment to writing clean and efficient code;

2. Proficient in Linux development environment and Python; experience with C++ is a strong advantage;

3. Hands-on experience with mainstream machine learning/deep learning frameworks such as TensorFlow or PyTorch;

4. Solid understanding of fundamental machine learning/deep learning concepts and familiarity with recommendation algorithms (e.g., collaborative filtering, exploration & exploitation, user interest modeling). Academic, research, internship, or industry experience in related areas is preferred;

5. Passionate about recommendation systems and live-streaming platforms; strong ability to learn quickly and solve problems independently;

6. Creative, innovative, and execution-oriented; motivated to explore cutting-edge technologies and implement them in real-world applications.