Define, track, and drive capacity efficiency targets across Facebook, Messenger, and Meta recommendation systems. Set targets with central Infrastructure and Finance, drive implementation with Capacity Tooling Engineers, and shift accountability to product teams.
Responsibilities
Manage $500M+ capacity efficiency programs with targets embedded in the company financial plan (long-range and mid-cycle financial planning processes) — own the translation between MW freed, $ICE saved, and capex impact Negotiate pre-fetched budget deductions with Finance and Infra using bottom-up technical feasibility (platform headroom, fulfillment timelines, supply constraints) Structure "net savings" commitments (~5% annual capacity return) across multiple top-level organizational budgets with quarterly cadence management Drive fluency across compute, storage, and GPU/AI resource types — reason about fleet-wide utilization (GPU SM utilization, memory efficiency, training vs. inference tradeoffs) and translate into product team action Navigate supply-constrained environments (DRAM/HDD shortages, AI build competition) — identify which efficiency levers (virtual capacity pooling, elastic resource allocation, heterogeneous hardware, and cloud offload) close specific supply gaps Translate program needs into engineering requirements for capacity tooling teams — drive adoption of defensive systems (pre-prod regression detection, prod regression recovery), automation (agentic efficiency at scale), and enforcement mechanisms (launch gates, policy-based quotas) Design operating models that shift efficiency from central-team-driven ad hoc efforts to product-team-owned commitments — build DRI/EffPOC structure, quarterly balance sheets, and launch-efficiency tradeoff frameworks Align product eng, infra platform, and finance stakeholders against shared outcomes at Director/VP level across all three organizations Translate fluently between product language ("unblock my launches"), infra language ("MW freed"), and finance language ("$ICE / capex impact")
Qualifications
Experience managing large-scale capacity or infrastructure efficiency programs ($500M+) Experience with infrastructure resource types including compute, storage, and GPU/AI systems Experience working across product engineering, infrastructure, and finance organizations Experience with financial planning processes (long-range and mid-cycle financial planning) and translating technical outcomes to financial impact Experience designing and implementing cross-organizational operating models and accountability frameworks Experience building measurement and tracking systems for efficiency programs Experience with large-scale capacity planning and efficiency tooling ecosystems Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) 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 codifying program operating rhythms that scale beyond individual contributors Experience with GPU utilization optimization (SM utilization, memory efficiency, training vs. inference tradeoffs) Experience navigating supply-constrained environments (DRAM/HDD shortages, AI infrastructure competition)