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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.

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.

This role will be based in Mountain View, CA.

The Network Infrastructure Observability team is responsible for delivering the platforms, tools, and insights that ensure our global network operates with high reliability, performance, and efficiency. We build large-scale data pipelines, real-time monitoring systems, and intelligent analytics that empower engineering and operations teams to detect anomalies, predict failures, and optimize network behavior. Our work directly impacts availability, capacity planning, and service health across all data center and backbone network environments.

As a Senior Staff Software Engineer, you will serve as a technical leader driving the architecture, innovation, and execution of next-generation observability systems for our network infrastructure. You will define long-term technical direction, lead cross-org initiatives, mentor senior engineers, and drive solutions for complex distributed systems challenges at massive scale. This role requires deep expertise in backend systems, data processing, and large-scale system design, with strong understanding of networking concepts.

Responsibilities:

+ Lead the architectural design and implementation of large-scale observability platforms, including telemetry ingestion, real-time analytics, network health monitoring, and anomaly detection.

+ Drive long-term strategy and roadmaps for network observability, ensuring alignment across infrastructure and network engineering teams.

+ Build and optimize data pipelines and streaming platforms capable of handling high-volume telemetry from data centers, backbone, and edge networks.

+ Partner with network domain experts to define meaningful SLIs/SLOs, improve network resiliency, and drive proactive detection of failures.

+ Develop automation, self-healing workflows, and intelligent alerting mechanisms to reduce operational toil and increase network reliability.

+ Collaborate with cross-functional engineering groups to ensure system interoperability, standardization, and seamless data exchange across infrastructure layers.

+ Mentor and guide engineers across teams, setting best practices for system design, code quality, and operational excellence.

+ Influence organizational strategy through technical leadership, design reviews, and cross-group technical forums.

+ Drive adoption of modern technologies and architectural patterns to improve latency, scalability, and observability coverage.

Basic Qualifications:

+ BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience

+ 10+ years of experience building and operating large-scale distributed systems or data-intensive backend platforms.

+ Experience with programming languages such as Go, Java, Python, C++, or similar.

+ Experience with streaming systems (Kafka, Flink, Spark Streaming, or similar) and high-throughput data pipeline architectures.

+ Experience with networking fundamentals: routing, switching, TCP/IP, network telemetry, SNMP, flow data, or similar.

+ Proven ability to lead complex technical initiatives end-to-end in a multi-team environment.

+ Background in system design skills with focus on scalability, reliability, and performance.

+ Experience with container platforms (Kubernetes), and microservices.

Preferred Qualifications:

+ Experience working in hyperscale or large distributed cloud environments.

+ Background in building observability stacks (metrics, logs, traces) or network monitoring platforms.

+ Familiarity with machine learning for anomaly detection or predictive analytics.

+ Experience with infrastructure automation or configuration management tools.

+ Experience with influencing across organizations (tech lead, architect, principal/IC leadership roles).

Suggested Skills:

+ Distributed Systems

+ Observability

+ Monitoring Systems

+ Technical Leadership

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $181,000 to $297,000. 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 https://careers.linkedin.com/benefits.

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 [email protected] and describe the specific accommodation requested for a disability-related limitation.

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.

Pay Transparency Policy Statement ​

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates ​

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.