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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Fellow, Software Engineering- Infrastructure - **Company:** LinkedIn Corporation - **Location:** San Francisco, CA, United States - **Salary:** $335,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Databases, Data Infrastructure, Data Integrity, Distributed Data Store, Distributed Systems, Online Databases, Open Source Technology, Reliability Engineering, Azure Machine Learning, Software Engineering, AI Infrastructure, Data Processing, Caching, AI Platforms, Kubernetes, Information Technology, Apache Kafka, Data Management, Machine Learning Operations, Hardware Infrastructure, Data Pipelines - **Published:** June 29, 2026 - **Apply:** https://www.juju.com/job/00000000gcbeu3 ## About the Role + Master's or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent exceptional research or industry experience + 20+ years of experience in software engineering with a sustained focus on infrastructure, distributed systems, Storage, databases, or related areas Preferred Qualifications + Exceptional breadth and depth of expertise spanning multiple infrastructure domains - including distributed systems, service delivery, compute and storage infrastructure, data platforms, online databases, caching, and AI/ML infrastructure - at scale + History of defining technical direction for large engineering organizations and influencing industry practices + Demonstrated ability to anticipate multi-year technical challenges and architect transformative solutions + Recognized technical authority with a track record of company-wide or industry-wide impact + Industry recognition through seminal publications, patents, major open-source contributions, or conference keynotes + Proven ability to mentor and elevate Distinguished Engineers and Principal Staff Engineers + Strong record of translating deep technical vision into platform and business outcomes at company scale + Experience leading infrastructure modernization and AI transformation initiatives at large scale + Experience driving developer productivity at scale - including tooling, frameworks, and platform investments that measurably improve how engineers build, test, and deploy software across a large engineering organization + Deep experience driving infrastructure efficiency, fungibility, and agility at hyperscale - including capacity planning, cost attribution, and compute optimization across large-scale infrastructure environments + Familiarity with the full infrastructure stack: physical and hardware infrastructure, compute orchestration, networking, storage, data processing pipelines, online serving systems, and AI/ML platforms + Experience working across open-source communities to participate in and influence cutting-edge projects relevant to infrastructure (e.g. Kubernetes, Envoy, Kafka, or equivalent) + Experience leading high-impact, cross-company initiatives Suggested Skills + Distributed Systems Architecture + Developer Productivity + Full-Stack Infrastructure Leadership + AI-Native Infrastructure + Industry Thought Leadership + Open Source Leadership + Data Infrastructure and Platforms + Reliability Engineering at Scale + Operational Excellence and Efficiency ## Description + Define and champion the long-term technical vision and strategy for LinkedIn's Infrastructure organization as a whole, influencing the direction of LinkedIn's entire engineering platform + Identify and solve the most complex and consequential technical challenges across the infrastructure stack - spanning compute, storage, networking, data pipelines, online serving, and AI infrastructure - at LinkedIn scale, setting a new bar for what is possible + Drive company-wide architectural decisions spanning service delivery, distributed databases, caching, data processing, AI training and serving infrastructure, and reliability systems, ensuring alignment with LinkedIn's multi-year technology roadmap + Establish the technical standards, design principles, and engineering culture that guide the Infrastructure organization and elevate engineering practices across LinkedIn broadly + Drive advancements in developer productivity - shaping the tools, frameworks, and engineering practices that enable LinkedIn's thousands of engineers to build, iterate, and ship software faster and more reliably + Lead infrastructure initiatives to increase efficiency, fungibility, and agility at hyperscale + Lead LinkedIn's infrastructure transformation toward AI-native paradigms - evaluating and adopting transformative technologies that position LinkedIn's Infrastructure as an industry leader in building and operating AI-first infrastructure at scale + Collaborate with Distinguished Engineers, Principal Staff Engineers, and executive leadership to align technical direction with business strategy and LinkedIn's infrastructure priorities including Operational Excellence, Modernization, and Efficiency + Advise LinkedIn executives on a broad range of technology, strategy, and operational decisions associated with infrastructure at global scale + Serve as a trusted advisor and mentor to the most senior technical talent across the organization, elevating the engineering bar company-wide + Represent LinkedIn externally as a preeminent thought leader - publishing research, presenting at top-tier conferences, contributing to landmark open-source projects, and engaging with the academic and industry communities + Drive cross-company initiatives that deliver lasting improvements to the reliability, scalability, efficiency, and performance of LinkedIn's infrastructure - spanning online availability, data integrity, compute utilization, and AI platform readiness + Anticipate and shape the evolution of infrastructure paradigms - including AI-driven operations, autonomous systems, and next-generation compute - that will define the industry over the next decade, 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. ## Related Videos - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Event based cache invalidation in GraphQL](https://www.wearedevelopers.com/videos/433-event-based-cache-invalidation-in-graphql) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [How to Answer the Interview Question: “Why Do You Want to Be a Software Engineer?”](https://www.wearedevelopers.com/magazine/392-how-to-answer-the-interview-question-why-do-you-want-to-be-a-software-engineer) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)