Senior Backend Engineer (Machine Learning Server Parameter) - EGO team
Summary
Builds and optimizes distributed Parameter Server systems for large-scale ML training/inference in search, ads, and recommendations, ensuring high-throughput parameter operations and real-time learning.
Job Description:
- Develop distributed Parameter Server (PS) systems for large-scale sparse model training and inference platforms in the search, advertising, and recommendation domains. The system should support high-throughput parameter read/write and update operations, handle hundreds of billions of features and TB-level sparse models, enable online real-time learning, and meet algorithmic needs such as feature admission and expiration.
- Participate in the development of the one-stop machine learning platform, integrating the PS system into the platform to provide a user-friendly, stable, high-performance, and platform-level distributed parameter service system. Enhance the platform's efficiency and usability, accelerating the model iteration process for algorithm teams.
Requirements:
- Bachelor's degree or above in Computer Science, Electronics, Automation, Software Engineering, or related fields, with at least 3 years of work experience.
- Proficient in C++ programming with strong low-level technical skills adept at multi-threaded programming, lock optimization, memory pool, thread pool, template programming, GDB debugging, performance tuning, and RPC frameworks.
- Familiarity with distributed PS systems, distributed system backend optimization, high-performance in-memory KV systems, KV storage systems based on NVMe-SSD, and high-performance client-server architecture systems is a plus.
- Highly passionate about computer technology, proactive in learning, with a strong spirit of in-depth research and hands-on practice. Maintains high standards and strict requirements for delivered code works with rigor and attention to detail.
- Strong team player with excellent continuous learning ability.