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Backend Software Engineer, Recommendation Content Understanding Architecture
About The Team
The Recommendation Architecture Middle - Platform Team is responsible for system architecture design in the fields of content understanding and content security for our company's products. The scope of work includes the design and development of the entire process of massive multi-modal content processing, retrieval, and sorting, ensuring the high availability of the system, and pursuing flexible abstraction and sustainable iteration.
Responsibilities:
1. Be responsible for the service construction and iteration of the multi-modal content understanding pipeline, solve core problems such as service availability, throughput and storage bottlenecks, and improve the development efficiency of new features.
2. Regarding the repetition and similarity issues of massive video content, closely collaborate with the algorithm team to continuously promote the application of industry-leading video representation models and large-scale vector retrieval technologies at the business level, and overcome engineering problems during the implementation process.
3. Abstract and enrich the multi-modal vector computing components, optimize the model inference performance of vector computing, enhance the stability, performance, and scalability of the system, and strengthen platform-wide integration.
The Recommendation Architecture Middle - Platform Team is responsible for system architecture design in the fields of content understanding and content security for our company's products. The scope of work includes the design and development of the entire process of massive multi-modal content processing, retrieval, and sorting, ensuring the high availability of the system, and pursuing flexible abstraction and sustainable iteration.
Responsibilities:
1. Be responsible for the service construction and iteration of the multi-modal content understanding pipeline, solve core problems such as service availability, throughput and storage bottlenecks, and improve the development efficiency of new features.
2. Regarding the repetition and similarity issues of massive video content, closely collaborate with the algorithm team to continuously promote the application of industry-leading video representation models and large-scale vector retrieval technologies at the business level, and overcome engineering problems during the implementation process.
3. Abstract and enrich the multi-modal vector computing components, optimize the model inference performance of vector computing, enhance the stability, performance, and scalability of the system, and strengthen platform-wide integration.