Responsibilities

Team Introduction

The Recommendation Architecture Data Platform (Offline Computing) Team is Responsible for the design and development of the offline computing systems for the recommendation architectures that power products with over 1 billion users, including Douyin, Toutiao, and Xigua Video. The role focuses on ensuring system stability and high availability, abstracting general-purpose real-time computing systems, and building unified recommendation feature and sample platforms. It also involves constructing flexible and scalable high-performance storage systems and computing models, continuously addressing paradigm shifts in recommendation systems, especially in the era of large models.

Core Responsibilities

• Design and implement efficient offline computing systems for large-scale recommendation systems.
• Design and develop flexible, scalable, stable, and high-performance storage systems and computing models.
• Conduct troubleshooting on production systems, and design and implement necessary mechanisms and tools to ensure the overall stability of production environments.
• Build industry-leading streaming computing frameworks and other distributed systems to provide reliable infrastructure for massive data and large-scale business systems.

Qualifications

Minimum Qualifications

• Bachelor's degree or above, majoring in Computer Science, or related fields, with at least 5 years of relevant experience
• Deep understanding of big data computing systems, with hands-on experience in Flink, Spark, Paimon, Velox, and other components of the big data computing stack.
• Familiar with machine learning technology stacks, including core technologies such as Py Torch, LLMs, and multimodal systems, with practical experience.
• Practical experience with features and samples in search, advertising, and recommendation systems.
• Strong coding and troubleshooting skills. Proficient in programming languages like Java, C++, Scala, Python.

Preferred Qualifications

• Passion for tackling challenging, undefined problems and a strong enthusiasm for learning new technologies. Habitual in keeping up with new tech trends and regularly following the latest academic papers.