Meta is seeking a Capacity Planning Optimization Lead to join the Capacity Engineering team, where you will play a critical role in ensuring Meta's infrastructure scales reliably and efficiently to meet the demands of billions of users across Facebook, Instagram, WhatsApp, and emerging AI and metaverse workloads. In this role, you will develop and drive capacity modeling, performance benchmarking, and demand forecasting strategies that inform infrastructure investment decisions at scale. You will partner with product, infrastructure, and finance teams to translate workload growth signals into actionable capacity plans, ensuring Meta's server, network, and data center resources are optimally provisioned across all planning horizons.

Responsibilities

Develop and own capacity models and performance benchmarking frameworks that forecast infrastructure demand across compute, storage, and network resources for Meta's global fleet
• Analyze workload performance data and capacity utilization trends to identify bottlenecks, inefficiencies, and optimization opportunities across production infrastructure
• Define and drive capacity planning strategy and roadmaps for cross-functional programs spanning product engineering, infrastructure, and finance teams
• Build and refine demand forecasting methodologies that translate product growth signals and AI workload projections into infrastructure provisioning requirements
• Develop scenario analyses and trade-off recommendations to support multi-billion dollar infrastructure investment decisions at the executive level
• Partner with hardware, data center, and network engineering teams to align capacity supply plans with demand forecasts across strategic and tactical planning horizons
• Establish performance baselines and capacity thresholds for new and existing infrastructure platforms, informing procurement and deployment timelines
• Identify systemic risks in capacity coverage and proactively develop mitigation strategies to prevent capacity shortfalls across critical services
• Contribute to the development of tooling and automation that improves the accuracy, speed, and scalability of capacity planning and performance analysis workflows
• Communicate capacity planning insights, risks, and recommendations clearly to engineering leaders and executive stakeholders through structured written and verbal formats

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 8+ years of experience working in Infrastructure/Cloud/Hardware with a background in Strategy, Capacity Planning, Supply Chain Optimization, 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
• Business acumen with experience forming and refining hypotheses into actionable analysis and 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

Preferred Qualifications
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience building or significantly improving capacity planning tooling, simulation frameworks, or automation pipelines
• Experience with capacity planning or performance engineering for AI or machine learning training and inference workloads at scale
• Familiarity with data center supply chain constraints and their interaction with capacity demand planning cycles
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience developing executive-level communications and scenario analyses to support large-scale infrastructure investment decisions