Senior GPU Capacity Planner
Own day-to-day management, allocation, and bin-packing optimization of Crusoe Cloud's GPU fleet. Bridge technical infrastructure with GTM teams to maximize utilization and align commercial demand with datacenter capacity.
About the job
What You'll Be Working On
GPU Fleet Bin-Packing & Optimization
- Develop and execute advanced bin-packing strategies to optimize GPU resource allocations within data centers
- Minimize fragmentation and maximize cluster utilization
GTM & Pipeline Alignment
- Act as the primary technical capacity partner to Sales, Customer Success, and Solutions Engineering
- Review upcoming pipeline demands and map them to physical hardware constraints
Cross-Functional Capacity Execution
- Collaborate tightly with Fleet Management, Infrastructure Engineering, and Data Center Operations
- Ensure maximum uptime of customer workloads
Utilization Modeling & Metrics
- Design and implement models to track real-time GPU cluster headroom, workload densities, and allocation velocities
- Prevent supply bottlenecks
Strategic Demand Forecasting
- Synthesize commercial demand signals and large-scale AI training/inference architectural trends
- Inform hardware placement and scheduling
Automation Requirements
- Partner with Core Software Engineering to translate manual allocation processes into scalable, automated programmatic scheduling and visualization tools
What You'll Bring to the Team
- 3+ years of direct experience in Infrastructure Capacity Planning, Technical Product Management, or Systems Engineering with heavy exposure to machine-level resource scaling
- Experience working within a hyperscaler cloud environment (e.g., AWS, GCP, Azure, Oracle Cloud) or a specialized, large-scale AI/accelerated compute cloud fabric
- Solid foundational understanding of GPU topologies (e.g., NVIDIA H100/B200 ecosystems)
- Proven ability to communicate complex infrastructure and physical layout constraints clearly to business stakeholders like Sales and Customer Success
- Translate commercial requirements back to hardware engineers
- Bachelor's or Master's degree in Computer Engineering, Computer Science, Operations Research, Industrial Engineering, Data Science, or an equivalent quantitative field
Bonus Points
- Deep technical familiarity with distributed AI training frameworks and multi-tenant cloud storage dynamics
- Passion for green energy and aligning massive computing scales with environmental sustainability initiatives
Benefits
- Competitive compensation and equity packages
- Restricted Stock Units
- Paid time off, paid holidays & leave of absence programs
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
- Global travel insurance & emergency assistance
- Daily meals allowance
- Additional perks & programs specific to location
Compensation Range: $160,000 - $195,000 + Bonus. Restricted Stock Units are included in all offers.
Skills
Infrastructure Capacity Planning, Gpu Resource Management, Bin-Packing Optimization, Nvidia Gpu Topologies, Systems Engineering, Technical Product Management, Cross-Functional Collaboration, Demand Forecasting, Utilization Modeling
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