Job Description
Our Live Recommendation Architecture Team is responsible for building and optimizing the architecture for the live broadcast recommendation system to provide the most stable and best experience for our users. The team focuses on system stability, high availability, online services, offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid‑platform, and realizing flexible, scalable high‑performance storage and computing systems. We work closely with applied machine learning engineers to build scalable systems that support innovative algorithms and techniques. Successful candidates must be able to commit to an onboarding date by the end of year 2026.
Responsibilities- Build and maintain high performance online services for the Live Recommendation System.
- Build extremely efficient and reliable data pipelines for candidate generation, profile generation, training examples generation, realt...
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