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
- Optimize recommendation model memory usage and inference latency on GPU-based heterogeneous computing systems for live scenarios
- Design and implement high-throughput distributed training and inference pipelines for live broadcast recommendation models
- Perform GPU kernel profiling and performance tuning to optimize real-time inference throughput for live streaming workloads
Minimum Qualifications:
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline
- Solid programming skills in C++/CUDA/Python, with basic understanding of GPU architecture and distributed training
- Familiarity with deep learning frameworks and basic knowledge of recommendation systems
Preferred Qualifications:
- Experience building production-grade training and inference systems for large-scale models, especially for recommendation or live streaming scenarios
- Hands-on experience optimizing recommendation models for memory efficiency, latency, and throughput improvements
- Familiarity with distributed training frameworks (NCCL, Horovod, DeepSpeed, FSDP)