Staff Software Engineer - Compute Infrastructure

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.  

LinkedIn operates one of the largest privately managed compute infrastructures in the world outside the public cloud providers. As a Staff Software Engineer on the Compute Infrastructure team, you will help build and evolve the platform that powers LinkedIn at every layer — the real-time services behind products serving over a billion members, large-scale data and analytics pipelines processing petabytes daily, and the AI/ML infrastructure driving recommendations, search relevance, and generative AI at scale.

The Compute Infrastructure stack is LinkedIn's internal private cloud, purpose-built to meet the company's unique scaling challenges. The platform is deeply Kubernetes-centric and continuously evolving to stay at the forefront of industry trends. You will work on problems that span large-scale workload orchestration, multi-cluster operations, platform reliability and efficiency, GPU compute optimization for AI/ML, and fleet health at scale — all at a scale that few organizations in the world encounter.

In this role, you will design and implement solutions that enable LinkedIn to scale its compute infrastructure to meet the demands of a rapidly growing user base and an ever-expanding product portfolio. You will work closely with a team of talented engineers to develop and operate systems that are robust, scalable, and efficient. You will also collaborate with cross-functional teams and thrive in a fast-paced, dynamic environment.

Responsibilities

• Enable thousands of LinkedIn engineering teams to deploy and operate reliably — driving impact across areas such as CI/CD pipelines that deliver a seamless developer experience, a workload platform that provides resilient orchestration at scale, a self-healing host management system with intelligent health detection and automated remediation, Kubernetes cluster lifecycle and infrastructure, and GPU compute infrastructure powering AI/ML workloads, all underpinned by intelligent autoscaling, resource allocation, and capacity management

• Accelerate LinkedIn's product velocity by improving platform observability, deployment safety, and release throughput

• Shape technical direction across team boundaries, ensuring the platform evolves ahead of demand from new products, AI workloads, and growth

• Strengthen the team by mentoring engineers, raising the technical bar, and fostering a culture of engineering excellence

• Resolve deep production issues that cut across Kubernetes internals, operating system behavior, and distributed system interactions