Automated Warehouse Operations Engineer

Meta is seeking an experienced and results-oriented Automated Warehouse Operations Engineer, who will be responsible for the end-to-end launch and operation of all new automated warehouses across Meta's centralized warehouse network. This position acts as the owner for new-site standups, ensuring each facility is built for optimal space utilization, material handling equipment (MHE) integration, automation readiness, and compliance with Meta's company standards. You will drive layout and capacity decisions, absorb the full scope of warehouse stand‑up work, and directly support the anticipated centralized warehouse growth. This role is crucial for ensuring that new automated facilities are designed correctly, delivered on time, and built to specification as Meta scales its storage and distribution operations across our warehouses, data centers, and POP/Hut network. Bachelor's degree in Industrial, Mechanical, or Manufacturing Engineering, Supply Chain, or a related field, or equivalent practical experience 7+ years of experience in warehouse/distribution center design, industrial engineering, or facility layout Experience creating 2D/3D CAD layouts (AutoCAD, Revit, Sketch Up, or equivalent) Experience specifying storage media (pallet rack, shelving, VLMs) and material handling equipment (MHE) Experience performing capacity, slotting, and material‑flow analysis Willingness to work on‑site and travel to new‑site stand‑ups (up to 50%) Experience designing automated or robotics‑enabled warehouses (ASRS, AMRs, conveyance) Experience with greenfield warehouse stand‑up and WMS configuration Knowledge of SOX inventory controls, HAZMAT, and FAI requirements PMP certification or equivalent project management experience 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) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)