Machine Learning Engineer, Search Recommendation
TikTok.com
Office
San Jose, California, United States
Full Time
About the Team:
The Search Growth team is at the forefront of developing the search recommendation algorithm for TikTok's rapidly expanding global e-commerce enterprise. Utilizing cutting-edge machine learning technology, advanced NLP, CV, recommendation, and multi-modal technology, we're shaping a pioneering engine within the industry. Our objective is to deliver the ultimate e-commerce search experience to over 1 billion active TikTok users worldwide. Our mission: to create a world where "there are no hard-to-sell, overpriced products."
Responsibilities - What You'Il Do
- Enhance search recommendation services and models for TikTok, driving increased search traffic on TikTok and TikTok Mall, while also improving user's search understanding.
- Optimize the recommender system based on hyperscale machine learning models, covering a range of tasks from recall/first-stage ranking to final-stage ranking in the end-to-end workflow.
- Explore the upper limits of short text recommendation and general recommendation technology, with a focus on the interaction between recommendation and NLP technology.
The Search Growth team is at the forefront of developing the search recommendation algorithm for TikTok's rapidly expanding global e-commerce enterprise. Utilizing cutting-edge machine learning technology, advanced NLP, CV, recommendation, and multi-modal technology, we're shaping a pioneering engine within the industry. Our objective is to deliver the ultimate e-commerce search experience to over 1 billion active TikTok users worldwide. Our mission: to create a world where "there are no hard-to-sell, overpriced products."
Responsibilities - What You'Il Do
- Enhance search recommendation services and models for TikTok, driving increased search traffic on TikTok and TikTok Mall, while also improving user's search understanding.
- Optimize the recommender system based on hyperscale machine learning models, covering a range of tasks from recall/first-stage ranking to final-stage ranking in the end-to-end workflow.
- Explore the upper limits of short text recommendation and general recommendation technology, with a focus on the interaction between recommendation and NLP technology.