Machine Learning Engineer Graduate (TikTok Local Service Search) - 2026 Start (BS/MS)

and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities

• Support the local video service business to enhance user discovery of life services such as hospitality, dining, and leisure.
• Improve the search experience in local services and promote ecosystem growth.
• Utilize large-scale machine learning techniques in search and recommendation scenarios with billions of users to: Improve user shopping experiences & Enhance conversion efficiency.
• 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.
• Utilize LLMs to understand users' complex local service search queries, summarize key points from numerous videos, comments, and POI reviews, and combine all those into structured AI search formats. Get involved in real-world AI applications, by building a revolutionized search experience for local service queries like destination choices, hotel searching, tourism, food & beverage reviews.

Qualifications

Minimum Qualifications:

• Final year or recent graduate with a background in computer science or a related technical discipline.
• Proficient coding skills and strong algorithm & data structure using C++/Python/Java.
• Have a solid knowledge of machine learning and practical experience in applying it.

Preferred Qualifications

• Effective communication and teamwork skills.
• Modeling experience in one or more of the following areas: Ads, Search engine, Recommender System, NLP/CV.
• Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and practical experience in areas such as single-modal/multi-modal LLM application and deployment.

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If you have any questions, please reach out to us at [email protected]