Large Model Knowledge System Architecture Engineer, ByteRec
Jun 5, 2025 Singapore
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
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Responsibilities
About The Team
The Volcano Engine Vector Database Knowledge Base team supports both internal and ToB customer requirements. We are responsible for the massive content processing of the Volcano Engine large model knowledge base and the effect tuning, evaluation and monitoring, and algorithm architecture optimization of the retrieval system architecture. We also provides technical solutions in framework construction, new feature delivery, and vertical solution construction, including:
Responsibilities
1. Optimize the architecture of the knowledge system around the capabilities of large models and knowledge bases, and design and reconstruct the measurement system and industry delivery system for the capabilities of the knowledge system;
2. Participate in the technical solution design and development of the framework and middleware of the large model RAG architecture to achieve the feature delivery and demand support of the Public Cloud product;
3. Based on the application scenarios of large models, link up and down the ecosystem, provide a general-purpose domain Knowledge Base solution, encapsulate common patterns and processes, and provide stable external service capabilities;
4. Explore the design and development of the MultiModal Machine Learning Knowledge Base;
Qualifications
Minimum Qualifications:
1. Bachelor's Degree in Computer Science or related fields
2. Solid programming skills, familiar with Python language, good code design style and coding ability, and good ability to solve practical problems;
3. Have a certain understanding of the RAG field, understand the common design paradigms of large model RAG knowledge systems, have a basic understanding of large model application frameworks, and have interest and enthusiasm for the implementation of large model applications;
Preferred Qualification:
1. Experience in participating and contributing to open source communities
About The Team
The Volcano Engine Vector Database Knowledge Base team supports both internal and ToB customer requirements. We are responsible for the massive content processing of the Volcano Engine large model knowledge base and the effect tuning, evaluation and monitoring, and algorithm architecture optimization of the retrieval system architecture. We also provides technical solutions in framework construction, new feature delivery, and vertical solution construction, including:
Responsibilities
1. Optimize the architecture of the knowledge system around the capabilities of large models and knowledge bases, and design and reconstruct the measurement system and industry delivery system for the capabilities of the knowledge system;
2. Participate in the technical solution design and development of the framework and middleware of the large model RAG architecture to achieve the feature delivery and demand support of the Public Cloud product;
3. Based on the application scenarios of large models, link up and down the ecosystem, provide a general-purpose domain Knowledge Base solution, encapsulate common patterns and processes, and provide stable external service capabilities;
4. Explore the design and development of the MultiModal Machine Learning Knowledge Base;
Minimum Qualifications:
1. Bachelor's Degree in Computer Science or related fields
2. Solid programming skills, familiar with Python language, good code design style and coding ability, and good ability to solve practical problems;
3. Have a certain understanding of the RAG field, understand the common design paradigms of large model RAG knowledge systems, have a basic understanding of large model application frameworks, and have interest and enthusiasm for the implementation of large model applications;
Preferred Qualification:
1. Experience in participating and contributing to open source communities
About The Team
The Volcano Engine Vector Database Knowledge Base team supports both internal and ToB customer requirements. We are responsible for the massive content processing of the Volcano Engine large model knowledge base and the effect tuning, evaluation and monitoring, and algorithm architecture optimization of the retrieval system architecture. We also provides technical solutions in framework construction, new feature delivery, and vertical solution construction, including:
Responsibilities
1. Optimize the architecture of the knowledge system around the capabilities of large models and knowledge bases, and design and reconstruct the measurement system and industry delivery system for the capabilities of the knowledge system;
2. Participate in the technical solution design and development of the framework and middleware of the large model RAG architecture to achieve the feature delivery and demand support of the Public Cloud product;
3. Based on the application scenarios of large models, link up and down the ecosystem, provide a general-purpose domain Knowledge Base solution, encapsulate common patterns and processes, and provide stable external service capabilities;
4. Explore the design and development of the MultiModal Machine Learning Knowledge Base;
Qualifications
Minimum Qualifications:
1. Bachelor's Degree in Computer Science or related fields
2. Solid programming skills, familiar with Python language, good code design style and coding ability, and good ability to solve practical problems;
3. Have a certain understanding of the RAG field, understand the common design paradigms of large model RAG knowledge systems, have a basic understanding of large model application frameworks, and have interest and enthusiasm for the implementation of large model applications;
Preferred Qualification:
1. Experience in participating and contributing to open source communities
Responsibilities
ResponsibilitiesAbout The Team
The Volcano Engine Vector Database Knowledge Base team supports both internal and ToB customer requirements. We are responsible for the massive content processing of the Volcano Engine large model knowledge base and the effect tuning, evaluation and monitoring, and algorithm architecture optimization of the retrieval system architecture. We also provides technical solutions in framework construction, new feature delivery, and vertical solution construction, including:
Responsibilities
1. Optimize the architecture of the knowledge system around the capabilities of large models and knowledge bases, and design and reconstruct the measurement system and industry delivery system for the capabilities of the knowledge system;
2. Participate in the technical solution design and development of the framework and middleware of the large model RAG architecture to achieve the feature delivery and demand support of the Public Cloud product;
3. Based on the application scenarios of large models, link up and down the ecosystem, provide a general-purpose domain Knowledge Base solution, encapsulate common patterns and processes, and provide stable external service capabilities;
4. Explore the design and development of the MultiModal Machine Learning Knowledge Base;
Qualifications
QualificationsMinimum Qualifications:
1. Bachelor's Degree in Computer Science or related fields
2. Solid programming skills, familiar with Python language, good code design style and coding ability, and good ability to solve practical problems;
3. Have a certain understanding of the RAG field, understand the common design paradigms of large model RAG knowledge systems, have a basic understanding of large model application frameworks, and have interest and enthusiasm for the implementation of large model applications;
Preferred Qualification:
1. Experience in participating and contributing to open source communities
Additional Information
- Posted: Jun 5, 2025