Product Manager, Compute NPI

ABOUT FLUIDSTACK We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! ABOUT THE ROLE We're hiring a Product Manager to lead NPI (New Product Introduction) for GPU infrastructure, working closely with datacenter, infrastructure, and networking teams to introduce new GPU SKUs and compute offerings. You'll define how Fluidstack evaluates, qualifies, and brings new GPU generations to market—from NVIDIA Blackwell and Rubin to AMD MI300X and future accelerators. This is a highly cross-functional role requiring deep technical judgment, vendor relationship management, and an understanding of how hardware capabilities map to customer workload requirements. You'll ensure Fluidstack maintains its competitive edge by offering the right mix of compute options optimized for training, inference, and specialized AI workloads. WHAT YOU'LL DO - Own the NPI roadmap for GPU SKUs, including evaluation criteria, qualification timelines, and go-to-market strategy for new hardware generations - Partner with datacenter teams to define requirements for power delivery (HVDC/LVDC), cooling (liquid vs. air), rack architecture, and physical infrastructure needed for next-gen GPUs - Work with infrastructure engineers to validate hardware performance across key dimensions: training throughput (MFU), inference latency (TTFT, TBT), memory bandwidth, interconnect topology (NVLink, InfiniBand) - Drive vendor engagement with NVIDIA, AMD, and emerging XPU providers—conducting technical deep dives, negotiating supply agreements, and managing early access programs - Define product specifications for system configurations: single-GPU instances, multi-GPU nodes, full rack deployments, and megacluster topologies - Analyze customer workload profiles to determine optimal GPU mix: H100 for large model training, L40S for inference, B200 for frontier research, MI300X for cost-sensitive workloads - Build business cases for new SKU introductions, including CapEx requirements, depreciation models, utilization forecasts, and competitive pricing analysis - Create technical documentation and benchmarking reports that help customers select the right GPU for their use case - Monitor GPU availability, supply chain constraints, and allocation strategies to ensure Fluidstack can meet customer demand while maintaining healthy margins - Collaborate with networking teams to ensure interconnect fabric (RoCE, InfiniBand) scales with GPU performance and supports distributed training patterns ABOUT YOU - 5+ years product management experience with at least 3 years focused on infrastructure, hardware platforms, or cloud compute services - Strong technical background in GPU architecture, accelerator performance characteristics, and AI workload requirements - Experience managing NPI processes from evaluation through production deployment—including vendor relationships, qualification testing, and rollout planning - Deep understanding of datacenter infrastructure: power distribution, thermal management, rack design, and high-density deployment constraints - Track record of making build vs. buy decisions on hardware platforms based on TCO analysis, competitive positioning, and customer demand signals - Familiarity with GPU performance metrics (TFLOPS, HBM bandwidth, TDP, MFU) and how they translate to real-world training and inference performance - Ability to work with engineering teams to debug hardware issues, analyze telemetry data, and identify root causes of performance degradation - Experience conducting competitive analysis of cloud GPU offerings from AWS, GCP, Azure, CoreWeave, Lambda Labs, and other specialized providers - Comfortable navigating supply chain complexity, allocation negotiations, and procurement timelines with hardware vendors - Bonus: Experience with networking topologies (fat tree, rail-optimized), storage systems (NVMe, Ceph), or HPC infrastructure design COMPENSATION To provide greater transparency to candidates, we share base pay ranges for all US-based job postings. Our compensation package includes base salary