| Location | Palo Alto, California |
What you'll do
1Participate 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.
2Drive 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.
3Ensure 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.
4Develop 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
1Bachelor's degree or above in a relevant field; 3+ years of relevant work experience in computer science.
2Proficient in Java or Go; solid foundation in concurrent programming, distributed systems, and performance optimization.
3Familiar 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.
4Familiar with foundational open-source components such as Linux, networking, storage, and message queues; deep understanding of implementation principles preferred.
5Bonus: Familiarity with AI-related technologies including but not limited to: PyTorch, Spring AI, Langfuse, LLM-based tooling.
6Strong problem-solving, communication, and cross-team collaboration skills; eager to learn and stay current with industry trends.
7Fluent in both English and Chinese (spoken and written).