Meta runs one of the largest server fleets on earth, and every product bet (AI training, ranking, inference, storage) turns into a demand for compute that we must forecast, shape, and match to a physical supply of servers, racks, power, and data center space. As the principal technical leader for Server Demand Planning across Infrastructure, you own the demand side of the plan and the feasible supply requirements it drives: you forecast long- and near-term server capacity demand by rack/hardware type and region, and you build the operations-research models that match that demand to supply so we land the right servers, in the right place, at the right time. You set Meta's technical approach to server demand planning, decide when to ship a production-grade optimization system versus a fast lightweight model to unblock a decision, and set direction across product/service capacity owners, capacity engineering, supply chain, data center planning, and finance, partnering directly with org leaders and growing the technical bench behind you.

Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years applying operations research / management science to real planning problems in demand planning, capacity planning, supply-demand matching, network optimization, or inventory MS in a quantitative field (Operations Research, Industrial Engineering, Applied Math, Statistics, CS, or related), or equivalent experience Deep operations-research toolkit: mathematical optimization (LP, MILP, stochastic/robust optimization), simulation, queuing theory, and probabilistic/statistical forecasting Demonstrated ability to build BOTH production-grade models/systems (deployed, maintained, driving real decisions) AND lightweight/prototype models delivered fast under ambiguity Experience reconciling forecasted demand against constrained supply, lead times, and inventory in a planning or capacity environment: reconciling forecasted demand against constrained supply, lead times, and inventory Fluency with optimization solvers (Gurobi, CPLEX, Xpress, or OR-Tools) and with SQL + Python for modeling, analysis, and pipelines Experience with planning platforms (Kinaxis, SAP IBP, o9, Blue Yonder, Demantra, E2open) and internal capacity tools (internal capacity planning and supply-matching tools) PhD in Operations Research, Industrial Engineering, Management Science, or a related quantitative field Statistical/ML forecasting depth (time series, hierarchical/probabilistic forecasting, forecast reconciliation) Direct experience with server/compute or data center capacity planning at hyperscale Publications, patents, or recognized technical leadership in OR / optimization / forecasting