Capacity Infrastructure Engineer

Company Description

Linked In 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 Linked In, 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 Linked In office on select days, as determined by the business needs of the team. This role will be hybrid in Linked In’s Mountain View, CA campus.

The Capacity Engineering team shapes how Linked In’s infrastructure footprint is planned, scaled, and optimized to power reliable, high-performance services. We focus on forecasting usage patterns, baselining key capacity metrics, and uncovering efficiency opportunities — directly driving multi-million-dollar cost savings. This is a high-visibility team where engineering decisions influence how Linked In allocates its $1B+ infrastructure budget each year.

We are looking for a Staff Capacity Infrastructure Engineer who will partner with Product and Infra teams to model demand, optimize capacity, and guide resource allocation across critical platforms.

• Own end-to-end capacity planning for critical infrastructure domains (e.g., compute, storage, databases), spanning demand forecasting, supply planning, and execution to support Linked In’s growth.
• Lead quarterly and annual capacity planning cycles, including scenario modeling, tradeoff analysis, and executive-ready recommendations that inform infrastructure investment decisions.
• Develop and standardize capacity forecasting models and core planning metrics, ensuring consistent definitions and usage across Product, Infrastructure, and Finance teams.
• Identify and drive optimization of stranded or inefficient capacity, partnering cross-functionally to reclaim resources and deliver meaningful cost savings.
• Translate product roadmaps, growth signals, and technology changes into infrastructure and cost impact, influencing long-term platform and hardware strategy.
• Build scalable planning frameworks, tools, and automation that improve forecast accuracy, reduce manual effort, and enable capacity planning to scale with Linked In’s infrastructure footprint.

• BA/BS Degree in Computer Science, Industrial Engineering, Operations Research, or related technical discipline, or equivalent practical experience.
• 8+ years of experience in capacity planning, infrastructure planning, or large-scale systems planning in distributed production environments.
• 5+ years of experience with data analysis and modeling tools (e.g., SQL, Python, spreadsheets or equivalent internal tooling).

• Strong executive communication skills, including the ability to present tradeoffs, risks, and recommendations succinctly.
• Experience leading annual and quarterly planning cycles involving Finance, Product, and Infrastructure leadership.
• Experience influencing cross-functional stakeholders without direct authority.

• Capacity Planning
• Infrastructure Optimization
• Stakeholder Management

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.

Linked In is committed to fair and equitable compensation practices.

The pay range for this role is $170,000 to $277,000. 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…