Our Live Recommendation Architecture Team is responsible for building up and optimizing the architecture for live broadcast recommendation system to provide the most stable and best experience for our users. The team is responsible for system stability and high availability, online services and offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid-platform, realizing flexible and scalable high-performance storage and computing systems. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques.
We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Design and implement offline and real-time data processing architectures for live broadcast recommendation systems
- Build distributed storage and stream computing systems optimized for high-volume real-time live streaming data processing
- Develop real-time data analytics platforms to support live broadcast business operation and algorithm iteration
Minimum Qualifications:
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline
- Familiarity with big data frameworks including Hadoop, Hive, Flink, Spark, Kafka, HBase, or RocksDB
- Proficiency in Java or C++ programming, with strong coding and troubleshooting abilities
Preferred Qualifications:
- Experience with real-time data processing or live streaming related systems
- Strong sense of ownership and ability to deliver results in fast-paced environments
- Good team collaboration skills and cross-cultural communication abilities