Meta is seeking a Performance and Capacity Engineer to drive infrastructure efficiency and reliability across Meta's global fleet of servers, data centers, and network resources. In this role, you will lead the development of processes and frameworks for pricing all virtual and physical resources at Meta, and overall Capacity Planning processes. You will operate at the intersection of software performance analysis, hardware utilization, and long-range capacity planning, ensuring Meta's products (including Facebook, Instagram, WhatsApp, and AI workloads) are supported by infrastructure that scales efficiently and reliably.

Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in a directly related field, or equivalent practical experience 16+ years of experience working in Infrastructure/Cloud/Hardware with a background in Strategy, Capacity Planning, or technology strategy consulting Experience leading enterprise-level Planning processes with frequent executive interaction Experience distilling complex data and high ambiguity into actionable insights and recommendations, to quickly separate signal from noise for executive communication and decision-making Experience applying business and financial analysis to form and refine hypotheses into actionable recommendations Experience identifying and framing complex or ambiguous infrastructure problems, defining opportunity spaces, and developing actionable strategies Experience in completing multiple cloud, infrastructure, or hardware optimization projects Direct experience designing and establishing new infrastructure planning processes from the ground up at a hyperscale cloud or infrastructure provider Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)