Machine Learning Engineer - Global E-commerce Recommendation/Search

The Data-E-commerce team oversees algorithmic and corresponding engineering work in the e-commerce business. Our responsibilities encompass not only utilizing recommendation and search algorithms to help users discover appealing products but also implementing risk control and content safety algorithms to identify malicious sellers/influencers and violations in products/videos/live streams, ensuring a secure shopping experience. Furthermore, we are involved in constructing intelligent customer service technology and large-scale product knowledge graphs, aiming to enhance efficiency across various transaction processes. Through operations optimization and intelligent supply chain algorithms, we work to improve logistics efficiency.

Responsibilities:

• Work in a team to conduct cutting-edge research in machine learning algorithms, such as retrieval and recommendation algorithms.
• Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users latent interests efficiently.
• Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safety. Minimum Qualifications
• BS/MS/PhD background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent analytical and problem-solving skills.
• Strong foundation in machine learning and deep learning, with experience in: NLP (Natural Language Processing). Personalization.
• Exceptional coding skills with solid knowledge of data structures and algorithms.
• Proficiency in Linux development environments.

Preferred Qualifications

• Prior experience in search, recommendation, or advertisement algorithms.
• Familiarity e-commerce businesses.