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.

Minimum Qualifications: 1. Bachelor’s Degree in Computer Science or related fields, with at least 3 years of relevant experience; 2. Have solid programming skills and basic computer knowledge, be familiar with Python/C++, have excellent design and coding styles, and possess strong abilities to solve practical problems. 3. Have good communication and collaboration skills, be enthusiastic about solving challenging problems, and be able to explore new technologies with the team to promote technological development. Preferred Qualification: 1. Have development experience related to recommendation, search, and advertising. Have experience in recall and ranking systems. 2. Have basic machine learning knowledge, be familiar with mainstream machine learning frameworks (PyTorch/TensorFlow). Have experience in high-performance computing or CUDA development.