Responsibilities

• Translate business and technical requirements into concrete performance test plans covering load, stress, scalability, and reliability scenarios.
• Analyze Java application performance at both architecture and code levels — identify bottlenecks, diagnose root causes, and drive optimization end-to-end.
• Design and implement architecture-level and code-level optimizations to enhance system throughput and stability.
• Work closely with DevOps to ensure production-level high availability across distributed systems.
• Independently troubleshoot real-time performance issues and deliver solutions with full ownership.
• Develop internal tools for automated performance monitoring, testing, and issue analysis.
• Review and validate AI-generated code for correctness and performance risks — leverage your engineering expertise to assess whether AI-assisted code meets production quality standards.

Requirements

• Strong ability to identify performance bottlenecks at system, service, and code levels, including deep proficiency in Java application performance analysis and JVM-level tuning (GC optimization, heap/thread analysis, profiling).
• Hands-on experience troubleshooting microservice performance issues in production environments.
• Practical experience with performance engineering tools such as K6 or JMeter.
• Proficiency with relational and NoSQL databases (MySQL, MongoDB), including query optimization and slow-query analysis.
• Familiarity with performance analysis for middleware such as Kafka, Redis, RabbitMQ.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Solid Linux skills — command-line and system-level troubleshooting.
• Programming language: Java (mandatory). Strong engineering instincts to evaluate code quality and performance implications.
• Strong passion for building systems that excel in performance, scalability, and reliability.
• We also welcome: 
• Java backend engineers looking to transition into QA — if you have deep Java development experience and a passion for performance engineering, this role is a great fit.
• New graduates with demonstrated Java and performance testing foundation.

Nice to have

• Experience at enterprise-grade distributed systems environments.
• Hands-on experience with AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) and ability to critically assess AI-generated code for performance and correctness.
• Experience with public cloud platforms (AWS, Azure, GCP, AliCloud).
• Hands-on experience setting up monitoring/observability stacks (Prometheus, Grafana).
• Linux kernel or OS-level performance analysis skills.
• Experience triaging and resolving production performance incidents.
• Ability to leverage APM tools (e.g., SkyWalking, Pinpoint, Jaeger) for performance troubleshooting.
• Rust programming experience.
• Experience working within distributed, remote teams.