Product Manager, Compute NPI

About Fluidstack

At Fluidstack, we’re building the infrastructure for abundant intelligence. We partner with top AI labs, governments, and enterprises - including Mistral, Poolside, Black Forest Labs, Meta, and more - to unlock compute at the speed of light.

We’re working with urgency to make AGI a reality. As such, our team is highly motivated and committed to delivering world-class infrastructure. We treat our customers’ outcomes as our own, taking pride in the systems we build and the trust we earn. If you’re motivated by purpose, obsessed with excellence, and ready to work very hard to accelerate the future of intelligence, join us in building what's next.

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.

• 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, Infini Band)

• 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 mega cluster 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 Cap Ex 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, Infini Band) scales with GPU performance and supports distributed training patterns

• 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, Core Weave, Lambda Labs, and other specialized…