可观测技术研发-国际化

1、负责可观测体系研发工作，围绕 Metrics、Logging、Tracing、Profiling 四大支柱，从全栈领域展开可观测基础能力建设； 2、负责监控平台、全链路追踪、日志服务、计算引擎（流式分析、实时告警、时序检测等）、告警、eBPF 等可观测相关技术架构及产品设计； 3、保障可观测相关基础服务，在高并发环境下的高性能、高可用，推动技术、产品持续优化迭代，支撑国内和海外可观测架构设计、数据合规、基建稳定性保障等工作； 4、落地 AI Infra 可观测、AI 应用可观测、可观测 AI+ 等相关技术，提高 AI 场景稳定性以及传统可观测产品使用体验和效率。 1、Participate in the end-to-end R&D of the observability platform across all four pillars — Metrics, Logging, Tracing, and Profiling — building full-stack observability infrastructure capabilities. 2、Drive the technical architecture and product design of monitoring platforms, distributed tracing, log services, compute engines (streaming analysis, real-time alerting, time-series anomaly detection, etc.), alerting systems, and eBPF-based observability technologies. 3、Ensure high performance and high availability of observability infrastructure under high-concurrency conditions. Drive continuous technical and product iteration to support observability architecture design, data compliance, and infrastructure stability for the multi-region environments. 4、Develop and implement AI Infra observability, AI application observability, and AI-powered observability capabilities to improve stability in AI scenarios and enhance the usability and efficiency of traditional observability products. 【任职要求】 1、全日制统招本科及以上学历，3 年及以上计算机相关工作经验； 2、精通 Java 或 Go 语言，熟悉并发编程、分布式系统、性能优化等，有扎实的编程基础； 3、熟悉云原生可观测性体系的相关产品及组件，包括不仅限于：OpenTelemetry、CAT、SkyWalking、Prometheus、VictoriaMetrics、ELK、ClickHouse、eBPF 等，了解 Kubernetes 基本原理并能运用； 4、熟悉基础技术开源组件，如 Linux、网络、存储、MQ 等，深入了解细节，掌握实现原理优先； 5、加分项：熟悉和使用 AI 场景相关技术，包括不仅限于：PyTorch、Spring AI、Langfuse、OpenClaw 等。 6、善于发现、解决问题，善于总结、思考、跨团队协作；关注业界新技术，好奇求知，有强烈的责任心及抗压能力； 7、中英文流利，能够在国际化团队环境中进行技术沟通与协作； 1、Bachelor's degree or above in a relevant field; 3+ years of relevant work experience in computer science. 2、Proficient in Java or Go; solid foundation in concurrent programming, distributed systems, and performance optimization. 3、Familiar with cloud-native observability products and components, including but not limited to: OpenTelemetry, CAT, SkyWalking, Prometheus, VictoriaMetrics, ELK, ClickHouse, eBPF; working knowledge of Kubernetes and its fundamentals. 4、Familiar with foundational open-source components such as Linux, networking, storage, and message queues; deep understanding of implementation principles preferred. 5、Bonus: Familiarity with AI-related technologies including but not limited to: PyTorch, Spring AI, Langfuse, LLM-based tooling. 6、Strong problem-solving, communication, and cross-team collaboration skills; eager to learn and stay current with industry trends. 7、Fluent in both English and Chinese (spoken and written).