About this opportunity
LinkedIn lists this Staff Software Engineer, Reliability opportunity in mountain view, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
Job Description
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 field or 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, accessibility and 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, partners and 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 experience beyond 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, data and 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 toil and 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
Additional Information
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. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. 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 at and describe the specific accommodation requested for a disability-related limitation.
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.
Pay Transparency Policy Statement
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link:
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Worksite address
mountain view, CA, 94039, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.