Staff Software Engineer, Reliability

LinkedIn•Mountain View, CA

2d•$156,000 - $255,000•Hybrid

About The Position

The Staff Software Engineer, Reliability will join LinkedIn’s Site Health Platform team within the Reliability Infrastructure organization. The team builds and operates the end-to-end incident management ecosystem across thousands of services and multiple regions, supporting incident detection and response, problem management, and post-incident learning. Its platforms help engineers coordinate response, capture context, and turn incidents and near misses into actionable insights that inform reliability improvements and organizational decisions. The role designs and evolves core incident management platforms, works in a critical on-call rotation, and partners with service owners and product teams to resolve high-severity incidents. It also applies AI and LLM-based systems to incident data, builds analytics to identify systemic reliability risks, and develops tools for fleet-wide data center and regional capacity stress testing. The engineer influences service architecture, SLOs, and reliability standards through technical leadership and platforms adopted across the organization. The position is based in Mountain View, California, and is hybrid: work is performed both from home and from a LinkedIn office on select days according to team business needs. The role offers a pay range of $156,000 to $255,000. Compensation may also include an annual performance bonus, stock, benefits, and/or other applicable incentive compensation plans.

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 and providing architectural leadership.
Experience building products and operating large-scale distributed systems.
Experience with at least two backend languages, such as Go, Python, or Java, and a track record of owning complex production systems.
Full-stack engineering experience building user-facing web applications and operational dashboards using modern frontend frameworks such as React.js, as well as 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.
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 communicating 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 engineer interfaces 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.
Ability to simplify complex workflows, reduce operational toil, and replace manual processes with well-designed automation.

Responsibilities

Design and evolve the core incident management platforms powering 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 raw incident data into actionable intelligence using AI and LLM-based systems, including automated summarization, classification, root-cause signals, and mitigation recommendations.
Build analytics and insights to surface systemic reliability risks, recurring failure patterns, and cross-service dependencies.
Build platforms and tools for realistic fleet-wide stress testing of data center and regional capacity, validating incident readiness across dependencies, traffic patterns, and growth scenarios.
Improve the consistency, clarity, and quality of 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.