About the Team
The Data-Search-TikTok-Local Services team enhances local services by improving user discovery of hospitality, dining, and leisure experiences while driving ecosystem growth. They leverage large-scale machine learning to refine search and recommendation systems, focusing on personalized relevance, CTR/CVR prediction, and optimized conversion efficiency for billions of users.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.
Responsibilities:
1. Support the local video service business to enhance user discovery of life services such as hospitality, dining, and leisure.
2. Improve the search experience in local services and promote ecosystem growth.
3. Utilize large-scale machine learning techniques in search and recommendation scenarios with billions of users to: Improve user shopping experiences & Enhance conversion efficiency.
4. Design and implement local services search algorithms across the full stack, including: Query analysis, relevance, recall, coarse ranking, fine ranking, and blended ranking. Personalized behavior modeling for relevance computation.
CTR (Click-Through Rate) prediction, CVR (Conversion Rate) prediction. Vector recall and value blending.Minimum Qualifications
1. Excellent analytical and problem-solving skills.
2. Strong foundation in machine learning and deep learning, with experience in: NLP (Natural Language Processing).
Personalization.
3. Exceptional coding skills with solid knowledge of data structures and algorithms.
4. Proficiency in Linux development environments.
5. Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
Preferred Qualifications
1. Prior experience in search, recommendation, or advertisement algorithms.
2. Familiarity with local life services and e-commerce businesses.