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.
About the RoleWe'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 doOwn 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
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
To provide greater transparency to candidates, we share base pay ranges for all US-based job postings. Our compensation package includes base salary, equity, benefits, and for applicable roles, commissions plans. Our cash compensation range for this role is $150,000-$250,000. Final offers vary based on geography, candidate experience, relevant credentials, and other factors. Outstanding candidates may be eligible for adjusted terms plus meaningful equity.
We are committed to pay equity and transparency.
Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email [email protected] with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.
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What We Do
Instantly reserve dedicated clusters of NVIDIA H200s and GB200s for any scale to supercharge your training and inference workflows.
