Sr. Staff Software Engineer, Systems Infrastructure (Kubernetes)

LinkedIn, the world's largest professional network, is seeking a Sr. Staff Software Engineer to join their Systems Infrastructure team, focusing on Kubernetes and compute infrastructure. This is a high-impact role based in either Mountain View, CA or Bellevue, WA, offering a hybrid work arrangement.

The position involves leading the re-architecture of LinkedIn's compute infrastructure stack, a critical initiative that will impact the entire engineering organization. The ideal candidate will have extensive experience in designing large-scale compute infrastructure and driving consensus among teams.

As a Sr. Staff Software Engineer, you'll be responsible for designing and implementing solutions that enable LinkedIn to scale its compute infrastructure to meet the demands of its growing user base. You'll work closely with distinguished engineers and technical fellows to develop robust, scalable, and efficient solutions.

Key responsibilities include contributing to LinkedIn's Compute infrastructure strategy, building and operating platforms for hardware resource allocation, and implementing high-performance scheduling/deployment solutions, including some of the world's largest Kubernetes clusters. The role requires strong technical leadership skills, as you'll be coaching and up-leveling a team of talented developers.

The position offers a competitive compensation package ranging from $180,000 to $300,000 annually, plus benefits including health and wellness programs, equity, and annual performance bonuses. This is an excellent opportunity for a senior technical leader who is passionate about building the underlying technology that powers one of the world's most-used internet applications.

The ideal candidate will have deep expertise in Kubernetes architecture, strong proficiency in Golang, and extensive experience with distributed systems and infrastructure management. You'll be working in a fast-paced, dynamic environment where you'll have the opportunity to make significant technical decisions that impact LinkedIn's infrastructure at scale.