Job Summary:
SHEIN Distribution Corporation is a global online fashion and lifestyle retailer committed to making fashion accessible to all. They are seeking a Senior Site Reliability Engineer to operate and evolve large-scale systems, ensuring high availability and reliability while driving improvements in system resilience and performance.
Responsibilities:
• Keep SHEIN’s mission-critical production systems running 24/7/365, participating in on-call rotations and acting decisively during incidents.
• Triage and resolve production incidents, leveraging AI-assisted log analysis and anomaly detection to accelerate root cause identification; drive continuous improvements that reduce MTTR and prevent recurrence.
• Monitor and manage capacity planning and resource utilization, partnering with cross-functional teams to ensure systems scale safely while remaining cost-effective.
• Own and operate core open-source infrastructure such as APISIX, Nginx, Kubernetes, Kafka, Elasticsearch, Redis, Consul, Etcd, Zookeeper and other large-scale distributed systems.
• Design, build, and maintain observability solutions (metrics, logs, traces, alerting), incorporating AI-powered anomaly detection and intelligent alert correlation to surface actionable signals from high-volume telemetry, improving system visibility and resiliency.
• Automate operational workflows and eliminate manual toil through scripting, tooling, and process improvements, including the use of AI-assisted development tools (e.g., Claude Code) to accelerate the building and iteration of internal operational platforms.
• Develop and maintain technical documentation, including runbooks, architecture diagrams, operational procedures, and on-call playbooks.
• Work closely with global engineering teams to improve infrastructure reliability and performance through better system design and operational discipline.
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Information Systems, or a related technical discipline, or equivalent practical experience.
• 3+ years of experience owning and operating large-scale, high-traffic, 24/7 production systems, ideally in cloud or cloud-native environments.
• Solid foundations in Linux, networking, and distributed systems, with the ability to debug complex production issues end to end.
• Hands-on experience with incident response, troubleshooting, and performance optimization in distributed systems.
• Experience applying AI/LLM-powered tools to reliability engineering, including designing and building automation or internal tools using AI-assisted development tools (e.g., Claude Code).
• Strong software engineering skills with experience building automation, tooling, or platforms in languages such as Python or Go.
• Experience operating or supporting open-source infrastructure components such as APISIX, Nginx, Kubernetes, Kafka, Elasticsearch, Redis, Consul, Etcd, Zookeeper, etc.
• Experience with observability and monitoring systems (Prometheus, Grafana, Zabbix, etc.) and performance analysis.
• Familiarity with Git, CI/CD pipelines, and configuration management tools (e.g., Ansible).
• A strong sense of ownership, a systematic approach to problem-solving, and a passion for making systems more reliable.
• Strong communication skills and the ability to collaborate effectively with geographically distributed teams.
Preferred:
• Bilingual fluency in Mandarin and English.
• Kubernetes Administrator certification or equivalent real-world experience.
• Experience operating big data platforms (Hadoop, Yarn, HBase, Hive, Spark).
• Experience applying AI/LLM-powered tools to reliability engineering, including designing and building automation or internal tools using AI-assisted development platforms (e.g., Claude Code).
Company:
SHEIN is a global online fashion and lifestyle retailer, offering SHEIN branded apparel and products from a global network of vendors, all at affordable prices. Founded in 2008, the company is headquartered in Los Angeles, California, US, , with a team of 1001-5000 employees. The company is currently Late Stage.