Staff Software Engineer - Reliability Platform & Incident AI

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. 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 through platforms, data, and technical leadership, ensuring improvements are durable, measurable, and adopted at scale. Basic Qualifications: Bachelor’s degree in Computer Science, Engineering, or related technical fieldor equivalent practical experience. Many postings also prefer or require an advanced degree (MS/PhD) for Staff-level roles. 6+ years of professional experience in software development, distributed systems, or reliability engineering. Experience leading technical projects/providing architectural leadership Experience building products and operating large-scale distributed systems. Experience with two or more backend languages such as Go, Python or Java with a track record of owning complex production systems. Full-stack engineering experience, including building user-facing web applications and operational dashboards using modern frontend frameworks such as React.js, along with backend APIs and data pipelines. Understanding of web development fundamentals including API design, performance, accessibilityand building intuitive interfaces for engineers and operational users. Understanding of reliability engineering principles, incident management, observability and operating systems under failure conditions. Demonstrated ability to lead technical design across teams, influence architecture beyond direct ownership and drive adoption through well-designed platforms. Debugging and root cause analysis skills, with the ability to communicate complex technical findings clearly to engineers, partnersand leadership. Preferred Qualifications: Experience applying AI or LLM-based techniques to operational or incident data, including automated summarization, classification, root cause hypothesis generation or reliability recommendations. Familiarity with vector databases and retrieval-based systems used to power context-aware analytics, search or agentic workflows. Frontend engineering experiencebeyond basic UI, including building data-dense, high-signal interfaces for engineers using React.js, modern state management and visualization libraries. Experience designing end-to-end full-stack systems where frontend, backend, dataand reliability concerns are considered holistically. Background in building internal developer platforms, observability tools, or incident response systems used at scale. A demonstrated ability to simplify complex workflows, reduce operational toiland replace manual processes with well-designed automation. Suggested Skills: High Severity Incident Response Production Troubleshooting & Root Cause Analysis Distributed Systems and Linux fundamentals Software Development (Go / Python / Java) & Architecture Observability & Incident Detection LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $156,000 to $255,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. 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Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: Documents in alternate formats or read aloud to you Having interviews in an accessible location Being accompanied by a service dog Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. 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