Summary
✨ AI‑Generated
Join an engineering team building a full-stack observability platform across metrics, logging, tracing, and profiling. You will contribute to monitoring architecture and design, distributed tracing, log services, streaming analysis, real-time alerting, time-series anomaly detection, and eBPF-based observability. The role emphasizes high performance, high availability, and infrastructure capable of handling heavy concurrent workloads.
Highlights
Build end-to-end observability infrastructure spanning metrics, logs, traces, and profiling. The role offers opportunities to shape monitoring architecture, distributed tracing, real-time analysis, alerting, anomaly detection, and high-performance infrastructure for high-concurrency workloads.
Description
About Rednote
rednote is one of the world's most trusted platforms, where over 350 million people share how they actually live, travel, create, and discover.
In a world of infinite content, what sets rednote apart is its source: real human experience, honestly lived and generously shared — one note at a time.
We are now writing our global chapter, and this team is at the forefront.
What You'll Do
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
Qualifications
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).
The Pay Range For This Role Is
200,000 - 400,000 USD per year(Palo Alto, CA)
