Our Recommendation Architecture Team is responsible for building and optimizing the architecture of the recommendation system to provide the most stable and best experience for users. The team focuses on optimizing the recommendation system architecture, ensuring stability and high availability, and improving the performance of both online services and offline data flows. Collaborating with the algorithm team, we work to enhance recommendation effectiveness and user experience, boost system performance while reducing costs, build data and service mid-platforms, and realize flexible and scalable high-performance storage and computing systems.
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 evolve scalable recommendation system architectures supporting multi-scenario business requirements across feeds and vertical content
- Build high-performance distributed storage systems and computing frameworks optimized for recommendation workloads
- Trouble-shooting of the production system, design and implement the necessary mechanisms and tools to ensure the stability of the overall operation of the production system
- Develop fault-tolerance mechanisms and observability tools to ensure production system stability and high availability
Minimum Qualification(s):
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline
- Strong programming skills in C++ or Python, with solid understanding of data structures and algorithms
- Familiarity with PyTorch or TensorFlow, and basic knowledge of Transformer architectures and Large Language Models (LLMs)
Preferred Qualification(s):
- Interest in recommendation systems, with basic understanding of recommendation principles or execution processes
- Experience with performance optimization or architecture optimization in high-traffic scenarios
- Quick learner with strong sense of product ownership and creative problem-solving skills