Cloud Capacity Planner

About the role

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the Role

We are seeking a Data Center Capacity Planner to translate business and AI infrastructure demand into an integrated capacity plan. You will determine where capacity is needed, when it must be available, and which constraints could affect delivery.

Working with Data Center leadership, infrastructure engineering, delivery program managers, supply chain, finance, and commercial teams, you will connect demand forecasts with available and planned supply. Your analyses will guide site expansion, equipment planning, and capacity allocation decisions.

Responsibilities

• Build and maintain near-term and long-range demand and supply forecasts by site and region, connecting AI compute requirements to system counts, racks, critical power, cooling, and network capacity.
• Reconcile customer commitments, expected growth, utilization, and engineering plans; distinguish committed demand from forecast opportunities and document planning assumptions.
• Maintain a clear view of installed, commissioned, allocated, available, and planned capacity. Account for operating reserves, redundancy requirements, and capacity that cannot yet support workloads.
• Model capacity gaps and expansion scenarios, incorporating utility timelines, construction and commissioning milestones, hardware availability, and supplier lead times.
• Recommend when to expand, reserve, or rebalance capacity. Partner with Finance to assess capital requirements, operating costs, utilization, and the tradeoffs between earlier availability and unused capacity.
• Partner with delivery TPMs to translate approved capacity plans into site requirements and milestones; update forecasts when schedules, demand, or infrastructure constraints change.
• Work with Data & Tooling partners to establish planning datasets, reconciliation checks, and dashboards. Track forecast accuracy, capacity coverage, utilization, and changes in expected availability.
• Lead recurring capacity reviews, explain material changes, and present decisions and recovery options to leadership with clear assumptions, owners, and dependencies.

Required Qualifications

• 5+ years of experience in capacity planning, infrastructure planning, operations research, supply and demand planning, or a related analytical role supporting complex infrastructure.
• Experience building demand and supply models across multiple locations or infrastructure environments, including scenario analysis and reconciliation of changing assumptions.
• Understanding of data center capacity constraints and dependencies across electrical power, cooling, space, hardware, networking, and deployment readiness.
• Strong SQL and spreadsheet modeling skills, with the ability to combine operational and financial data, validate results, and explain the limits of incomplete information.
• Ability to convert forecasts into specific recommendations and influence decisions across engineering, finance, program management, and operations.
• Clear written and verbal communication, including concise leadership updates that explain capacity gaps, timing risks, and decision tradeoffs.
• Bachelor’s degree in engineering, computer science, operations research, mathematics, supply chain, or a related field, or equivalent practical experience.
• Preferred Qualifications
• Experience planning capacity for AI, high-performance computing, hyperscale cloud, or colocation environments, including high-density infrastructure.
• Proficiency with Python or another analytical language for forecasting, optimization, and repeatable planning workflows.
• Experience with business intelligence tools, data center infrastructure management systems, enterprise resource planning systems, or infrastructure inventory platforms.
• Familiarity with power delivery timelines, liquid cooling, long-lead electrical and mechanical equipment, or global data center expansion.
• Experience connecting workload demand and service requirements to fleet utilization, capacity reservations, and investment planning.