crusoe - Staff Compute & Storage Capacity Planner

• WHAT YOU'LL BE WORKING ON • End-to-End Capacity Modeling: Own and manage the multi-quarter capacity plans and allocation models for core infrastructure resources across Compute (CPUs) and multi-tiered Storage platforms (File, Object, and Block arrays). • Demand Forecasting & Signal Analysis: Analyze demand signals, historical workload profiles, customer usage shifts, and product roadmaps to build highly precise resource consumption forecasts to guide strategic hardware placement strategies. • Cross-Functional Collaboration: Partner closely with Sales, Storage Engineering, Server Hardware Engineering, SRE, Product, Finance, and Supply Chain to translate high-level product forecasts into physically deployed hardware allocations while navigating global procurement lead times. • Efficiency & Financial Optimization: Interface directly with Finance and Product to tightly balance structural cost efficiency, infrastructure unit economics, depreciation schedules, and hardware utilization targets against technical availability cushions. • Automation Requirements: Define operational metrics, establish planning baseline standards, and partner with Software Engineering teams to architect, automate, and implement automated capacity workflows and real-time visualization dashboards. • Data Mining & Validation: Perform complex data mining and verification across raw hardware usage metrics, asset management databases, and financial billing pipelines to guarantee maximum structural accuracy in capital efficiency models. • Proven Capacity Domain Expertise: 3+ years of direct experience in Infrastructure Capacity Planning, Infrastructure Systems Engineering, or Technical Product Management with heavy exposure to compute and storage. • Deep Hardware & Architecture Familiarity: Demonstrated foundational technical knowledge of enterprise server configurations, CPU sizing/benchmarking, and storage across File, Object, and Block storage infrastructures. • Hyper-Growth Experience: Experience working inside high-growth, fast-paced public cloud environments or massively distributed systems topologies. • Excellent Communication: Strong structured narrative delivery, documentation, and cross-functional negotiation skills to successfully bridge alignment across engineering technicalities and business/financial constraints. • Education: Bachelor’s or Master's degree in Computer Engineering, Computer Science, Operations Research, Mathematics, Data Science, or an equivalent technical/quantitative field. • Experience building or defining foundational product requirements for custom in-house software automation tools used for machine-level monitoring, resource provisioning, or automated capacity modeling. • Willingness and capacity to scale scope into specialized GPU clusters and cutting-edge accelerated AI computing fabrics as the underlying infrastructure footprint expands.