Staff Software Engineer, Reliability

LinkedIn•Mountain View, CA

3d•$156,000 - $255,000•Hybrid

About The Position

LinkedIn’s Site Health Platform is part of the Reliability Infrastructure organization and supports the end-to-end incident management ecosystem. The team builds platforms that help detect issues, coordinate incident response, capture context, and turn outages and near misses into structured insights across thousands of services and multiple regions. Its mission is to improve availability for members and customers, provide engineers with a more proactive site-wide reliability ecosystem, and keep business and product owners informed about service disruptions. The Staff Software Engineer will design and evolve incident management platforms spanning incident response, mitigation, problem management, and prevention. The role includes serving in a critical on-call rotation, applying AI and LLM-based systems to incident data, building reliability analytics and capacity stress-testing tools, and influencing service architecture, SLOs, and reliability standards across teams. The work involves technologies, architectures, and systems hosted in data centers around the world. The position is based in Mountain View, California, and follows a hybrid work arrangement, with work performed both from home and from a LinkedIn office on select days according to team business needs.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
At least 6 years of professional experience in software development, distributed systems, or reliability engineering.
Experience leading technical projects or 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, and a track record of owning complex production systems.
Full-stack engineering experience, including user-facing web applications and operational dashboards using modern frontend frameworks such as React.js, backend APIs, and data pipelines.
Understanding of web development fundamentals, including API design, performance, accessibility, and intuitive interfaces for engineers and operational users.
Understanding of reliability engineering principles, incident management, observability, and operating systems under failure conditions.
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, including the ability to communicate complex technical findings clearly to engineers, partners, and leadership.

Nice To Haves

Experience applying AI or LLM-based techniques to operational or incident data, such as automated summarization, classification, root-cause hypothesis generation, or reliability recommendations.
Familiarity with vector databases and retrieval-based systems for context-aware analytics, search, or agentic workflows.
Frontend engineering experience beyond basic UI, including data-dense, high-signal interfaces for engineers using React.js, modern state management, and visualization libraries.
Experience designing end-to-end full-stack systems that holistically consider frontend, backend, data, and reliability concerns.
Background building internal developer platforms, observability tools, or incident response systems used at scale.
Demonstrated ability to simplify complex workflows, reduce operational toil, and replace manual processes with well-designed automation.

Responsibilities

Design and evolve core incident management platforms supporting the full incident lifecycle, from detection and response to problem management and prevention, across thousands of services and teams.
Participate in a critical on-call rotation and provide expert incident triage and coordination during high-severity outages.
Partner with service owners and product teams to diagnose issues, mitigate member impact, and drive timely resolution.
Transform unstructured incident data into actionable intelligence using AI and LLM-based systems, including automated summaries, classification, root-cause signals, and mitigation recommendations.
Build analytics and insights that identify systemic reliability risks, recurring failure patterns, and cross-service dependencies to support organization-wide prioritization.
Build platforms and tools for realistic fleet-wide stress testing of data center and regional capacity, validating readiness across dependencies, traffic patterns, and growth scenarios.
Drive consistency, clarity, and quality in incident declaration, management, review, and learning practices.
Influence service architecture, SLOs, and reliability standards through platforms, data, and technical leadership, ensuring improvements are durable, measurable, and adopted at scale.