Summary

Responsibilities

1. Participate in the end-to-end development of content recommendation systems, including problem definition, data exploration, system design, implementation, performance optimization, and deployment.
2. Build efficient and scalable recommendation services, implementing modules such as content recall, ranking, filtering, deduplication, and cold start handling.
3. Collaborate closely with product, data, and operations teams to drive user growth and improve content distribution effectiveness.
4. Analyze key metrics and logs of the recommendation system to identify bottlenecks and continuously improve system stability, performance, and service quality.
5. Stay up-to-date with new technologies in recommendation engineering to continuously enhance engineering efficiency and system maintainability.

Requirements

1. Bachelor’s or Master’s degree in Computer Science or a related field.
2. Around 5 years of experience in backend system or distributed service development; experience with large-scale online services is a plus.
3. Familiar with the engineering architecture of recommendation systems, with hands-on experience in implementing modules such as recall, ranking, filtering, and feature construction.
4. Solid programming skills with proficiency in Java; familiar with common service frameworks, caching, middleware (e.g., HBase, Elasticsearch), and RPC technologies.
5. Experience with large-scale data processing; familiarity with platforms such as Spark, Flink, or Hadoop is a plus.
6. Practical experience in system stability, observability, and performance tuning.
7. Strong team collaboration and communication skills, with a strong sense of ownership and a focus on engineering execution and continuous improvement.

Skills

• Community Moderator
• Development
• Java
• Software Architecture
• Software Engineering
• Team Collaboration