About this role
Full Time Machine Learning Engineer, AI Search - USDS in e-commerce at TikTok in San Jose, California, United States. Apply directly through the link below.
At a glance
- Work mode
- Hybrid
- Employment
- Full Time
- Location
- San Jose, California, United States
Core stack
- Machine Learning
- Cross-functional
- Infrastructure
- Scalability
- Innovation
- Efficiency
- Feedback
- LLMs
- NLP
- AI
Quick answers
Is this Machine Learning Engineer, AI Search - USDS job remote?
Yes, this position is fully remote (San Jose, California, United States).
What skills are required?
Machine Learning, Cross-functional, Infrastructure, Scalability, Innovation, Efficiency, Feedback, LLMs, NLP, AI.
TikTok is hiring for this role. Visit career page
San Jose, United States
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 search engines to provide the best search experience, for more than 1 billion monthly TikTok users around the world.
About the Role
TikTok is seeking an independent developer for the Search team. This role is pivotal in advancing TikTok's search capabilities by integrating cutting-edge recommendation algorithms and large language models (LLMs) technologies to deliver highly personalized and engaging user experiences.
Responsibilities
- Independent execution: Independent development, experiment, analysis and deployment of large-scale personalized search and recommendation systems, ensuring scalability, efficiency, and robustness.
- LLM Integration: Research and integrate LLMs within the recommendation pipeline to enhance content understanding, user intent recognition, personalization and content generation. Explore hybrid models that combine LLMs with traditional recommendation systems to mitigate feedback loops and uncover novel user interests.
- Innovation with Generative Recommendation Systems: Drive the integration of generative recommendation approaches, leveraging generative models to directly generate personalized content recommendations, moving beyond traditional ranking-based methods. This includes exploring frameworks like GenRec, which utilize LLMs/VLMs 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.
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 search engines to provide the best search experience, for more than 1 billion monthly TikTok users around the world.
About the Role
TikTok is seeking an independent developer for the Search team. This role is pivotal in advancing TikTok's search capabilities by integrating cutting-edge recommendation algorithms and large language models (LLMs) technologies to deliver highly personalized and engaging user experiences.
Responsibilities
- Independent execution: Independent development, experiment, analysis and deployment of large-scale personalized search and recommendation systems, ensuring scalability, efficiency, and robustness.
- LLM Integration: Research and integrate LLMs within the recommendation pipeline to enhance content understanding, user intent recognition, personalization and content generation. Explore hybrid models that combine LLMs with traditional recommendation systems to mitigate feedback loops and uncover novel user interests.
- Innovation with Generative Recommendation Systems: Drive the integration of generative recommendation approaches, leveraging generative models to directly generate personalized content recommendations, moving beyond traditional ranking-based methods. This includes exploring frameworks like GenRec, which utilize LLMs/VLMs 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.
Job details
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