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# Machine Learning Engineer, Search - USDS

- **Company:** Tiktok Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $136,800.0 - $359,720.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), C++ (Programming Language), Data Structures, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Search Technologies, Pytorch, Large Language Models, Generative AI, Information Technology, Search Engines, Software Library, Programming Languages
- **Published:** June 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=823b5444548540c6

## About the Role

Do you have experience in Machine learning libraries?, Do you have a Bachelor's degree?, Minimum Qualifications - Bachelor's or advanced degree in Computer Science, Machine Learning, or a related field. - Proficient in machine learning frameworks (e.g., TensorFlow, PyTorch), programming languages (e.g., Python, Java, C++), and deep understanding of data structures and algorithms. - 2+ years in developing large-scale search or recommendation systems (feed, search, advertisement, etc). Familiarity with generative recommendation frameworks and their application in large-scale systems is highly desirable. Preferred Qualifications - Prior experience in search, recommendation, or advertisement algorithms. - Publication records in journals or conferences.

## Description

About the Team The Search team is responsible for the machine learning algorithm for TikTok's rapidly growing video search, ecommerce search, local service search and AI search business. We use state-of-the-art large-scale machine learning technology, cutting-edge NLP, CV and multi-modal technology, and generative models (LLM, VLM) to build the industry's top-class search engines to provide the best search experience, for more than 1 billion monthly TikTok users around the world. Responsibilities - Improve the basic search quality and user experience: Optimize query analysis and text relevance matching. Understand video content and implement multi-modal matching. Improve users' perception of product authority, and deeply participate in the design and implementation of core search products. Comprehensively improve the end-to-end user experience. - Design and implement the end-to-end ranking system (recall, first stage ranking, final stage ranking and mixed row): Use massive behavioral
data to deeply model users' personalized interests. - Promote the development of the ecosystem: From the perspective of technology and businesses, solve challenging problems such as supply and demand matching, business cold start, and sustainable business growth, etc. Think, analyze and adjust the evolution of the system to achieve long-term and sustainable growth of GMV - Innovation with Generative Recommendation Systems: Drive the integration of generative recommendation approaches, leveraging LLMs to directly generate personalized content recommendations, moving beyond traditional ranking-based methods. This includes exploring frameworks like GenRec, which utilize LLMs to interpret user contexts and generate relevant recommendations. - Cross-functional Collaboration: Work closely with product managers, data scientists, infrastructure engineers, operation teams to enable search personalization strategies with overall product goals.


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