Expert Backend Engineer (Machine Learning Server Parameter) - EGO team

Shopee • Commerce • Bachelor's Degree • C++ • 4 hours ago
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Salary
≈ $87k – $217k per year (Estimated)
Location
In office (Singapore)
Seniority
Senior · 6+ years exp

Confirmed on the employer's own hiring board on Sep 24, 2026. First seen by Alion on Sep 24, 2026.

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Shopee

Shopee is a multinational technology company and e-commerce marketplace platform operating as a subsidiary of Sea Limited. The company provides a hybrid B2C and C2C digital marketplace offering retail products, integrated digital payments and financial services via ShopeePay/SeaMoney, and end-to-end logistics and fulfillment solutions through SPX Express. Headquartered in Singapore, the platform serves millions of consumers and merchant partners across Southeast Asia, Taiwan, and Latin America.
shopee.com • HQ: Singapore • Commerce • Est. 2015
  • 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.
  • Bachelor’s degree or above in Computer Science, Electronics, Automation, Software Engineering, or related fields, with at least 6 years of relevant 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.
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