Head of Global Compute Supply & Platform Strategy
Lead global compute strategy and platform operations for frontier AI/robotics training at Luma. Own multi-year capacity planning, vendor partnerships, megawatt-scale capital deployment, and infrastructure leadership for 10k+ accelerator fleets.
About the job
What You'll Do
- Architect Multi-Year Compute Strategy: Lead capacity planning, global vendor and cloud partnerships, on-prem vs. cloud mix, and accelerator supply chain roadmaps (H/B-series GPUs, custom silicon evaluation).
- Direct the Platform Org: Provide strategic leadership to our infrastructure, distributed systems, and datacenter operations teams—scaling the organization to support next-generation compute demands.
- Maximize Fleet Utilization: Oversee the architectural efficiency of our cluster configurations to deliver >50% Model Flops Utilization (MFU) on flagship training runs.
- Command a Megawatt Budget: Negotiate, secure, and operate our largest-scale capital deployments for compute infrastructure, partnering directly with Finance to optimize unit economics and risk management.
- Unify Global Capacity: Champion the platform strategy that enables world-model training, heavy simulation rollouts, and real-time on-robot inference to seamlessly share a single, elastic fleet.
- Act as Principal Executive Interface: Serve as the primary commercial and strategic bridge to NVIDIA, AMD, hyperscalers, and frontier silicon vendors.
Qualifications
- 10+ years of engineering leadership experience in large-scale distributed systems, infrastructure, or technical supply chain, with a proven track record of leading compute platform strategy at a frontier AI lab, hyperscaler, or major autonomy program.
- Deep technical & commercial fluency in high-performance cluster topology, high-speed interconnects (InfiniBand/RoCE), large-scale data systems, and the economics of distributed training architectures.
- Direct operational oversight of 10k+ accelerator environments in high-performance production settings.
Preferred Qualifications
- Experience orchestrating capital or infrastructure for training runs at the >100B-parameter or >100k-GPU-day scale.
- Familiarity with the unique capacity and latency demands of edge-to-cloud inference and real-time autonomous systems.
Compensation
- The base pay range for this role is $250,000 – $450,000 per year.
Skills
Large-Scale Distributed Systems, Infrastructure, Technical Supply Chain, Compute Platform Strategy, High-Performance Cluster Topology, InfiniBand, Roce, Large-Scale Data Systems, Distributed Training Architectures, Accelerator Environments, Nvidia, Amd, Hyperscalers, Capital Planning, Datacenter Operations
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