Staff Software Engineer, Reliability

About the role

This role will be based in Mountain View, CA. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. Site Health Platform sits at the core of LinkedIn’s Reliability Infrastructure organization, with a primary focus on the end-to-end incident management ecosystem. Our mission is for every member and customer to experience LinkedIn as "always on", every engineer to benefit from a more insightful and proactive site-wide reliability ecosystem, and every business and product owner to be well-informed about service disruptions as they occur. We own the full incident lifecycle across thousands of services and multiple regions, from incident response and mitigation, through problem management and post-incident learning. The platforms we build are the backbone of how LinkedIn detects issues, coordinates incident response, captures context, and turns outages and near misses into structured, actionable insights. By transforming incidents into data and learnings, we enable teams to systematically improve reliability over time. Our work informs engineering priorities, infrastructure investments, capacity planning, and executive decision-making, ensuring the network is dependable when it matters most. You will be exposed to many different technologies, architectures, and systems hosted in state-of-the-art data centers across the globe.

Responsibilities

• Designing and evolving the core incident management platforms that power LinkedIn’s full incident lifecycle, from detection and response to problem management and prevention, across thousands of services and teams.
• Serving in a critical on-call rotation, providing expert incident triage and coordination during high-severity outages.
• Partnering closely with service owners and product teams to diagnose issues quickly, mitigate member impact, and drive timely resolution under pressure.
• Transforming raw, unstructured incident data into clear, actionable intelligence using AI and LLM-based systems, including automated summarization, classification, root cause signals, and mitigation recommendations.
• Building analytics and insights that surface systemic reliability risks, recurring failure patterns, and cross-service dependencies, enabling org-level prioritization rather than isolated, service-by-service fixes.
• Building platforms and tools that enable realistic, fleet-wide stress testing of data center and regional capacity, validating incident readiness across dependencies, traffic patterns, and growth scenarios before they impact a significant production outage.
• Driving consistency, clarity, and quality in how incidents are declared, managed, reviewed, and learned from, raising the reliability bar across a large, fast-moving engineering organization.
• Influencing service architecture, SLOs, and reliability standards