Senior Vice President Product Management
5 days ago
Fremont
Role \n \n SVP, Product Management (Reporting to the CEO) \n \n Key Areas \n \n Data Centers, Infrastructure Engineering, GPU Cluster Management, High Speed Data Center Networks, BMS, Infrastructure Operations, People Management \n \n About the Role \n \n The SVP of Product Management will own AI Fabrik's end-to-end product strategy — product definition, portfolio planning, customer discovery, roadmap development, commercialization, go-to-market, pricing and packaging, partner strategy, and delivery. \n \n This executive is the primary connection among customers, the market, Infrastructure Engineering, Inference Software Engineering, sales, strategic partners, and leadership, translating customer needs into a roadmap covering data center capacity, GPU services, model availability, inference software, token delivery, and enterprise requirements. \n \n A critical responsibility is converting market and customer demand into infrastructure-development decisions — where AI Fabrik should expand, what sites and GPU capacity are required, and how investments support customer commitments and growth. The SVP will also build strategic relationships with GPU manufacturers, server OEMs, model developers, and cloud partners to create a cohesive infrastructure and token-delivery strategy that differentiates AI Fabrik. \n \n The successful candidate must have led a comparable product organization at a cloud provider, inference platform, GPU cloud, tokens-as-a-service business, or neo-cloud company, combining product leadership with technical depth in models, GPU architectures, data center capacity, and cloud economics — and must be a creative, unconventional leader who can rethink how AI infrastructure is packaged and delivered, rather than replicating traditional public-cloud products. \n \n Key Responsibilities \n \n\n • Product Strategy and Company Leadership: Own AI Fabrik's product vision, portfolio, and roadmap, including reserved/on-demand GPU capacity, managed inference, model APIs, and tokens as a service. Translate objectives into product priorities and investment decisions; define differentiation within the AI inference, GPU cloud, and neo-cloud markets. Serve on the executive leadership team and communicate strategy, performance, and risk to executives, investors, and customers.\n, • Product Definition and Portfolio Management: Define the full product portfolio and how services work together as an integrated inference platform, including reserved/on-demand GPUs, dedicated clusters, managed model endpoints, and token-based services. Establish requirements spanning compute, networking, model serving/routing, metering, billing, and security, and manage the full lifecycle from business case through launch and retirement.\n, • Integrated Product Roadmap and Delivery: Own the roadmap across infrastructure, GPU capacity, inference software, and commercial systems, working closely with the SVPs of Infrastructure and Inference Software Engineering to align priorities and delivery schedules. Coordinate dependencies among data center build-outs, procurement, and launches; run a disciplined prioritization process, lead readiness reviews, and hold teams accountable for outcomes.\n, • Customer Discovery and Requirements: Build direct relationships with strategic customers and enterprise leaders; run a structured discovery process covering workload characteristics and future demand. Translate requirements into reusable platform capabilities, participate in major negotiations, and ensure commitments are reflected in the roadmap.\n, • Data Center Capacity and Business Expansion: Translate demand forecasts into requirements for GPU capacity, power, cooling, and data center expansion, partnering with Infrastructure Engineering and finance on where and when to build. Build demand models connecting workloads and token volumes to infrastructure needs, evaluate latency/sovereignty/regulatory implications, and identify growth opportunities in edge and partner-operated infrastructure.\n, • Go-to-Market and Commercialization: Own the product-led go-to-market strategy with sales, marketing, and finance — segments, positioning, and routes to market. Develop packaging, pricing, and consumption/commitment models; partner with sales on major pursuits and track adoption, utilization, and margins to refine strategy.\n, • GPU OEM and Infrastructure Partner Strategy: Build relationships with GPU manufacturers, OEMs, and cloud partners, translating their roadmaps into AI Fabrik's plans. Develop joint hardware/software/token solutions, evaluate emerging hardware on performance and cost per token, and negotiate integrations while preserving an open architecture and avoiding single-vendor dependency.\n, • Model Provider and Token-Service Strategy: Build relationships with foundation-model developers and model-distribution platforms; define strategy for public, private, open-source, and customer-provided models. Develop a token-delivery strategy covering selection, API access, latency, and pricing, and define how AI Fabrik differentiates through performance, model choice, and open architecture.\n, • Pricing, Packaging, and Product Economics: Own pricing and packaging for the full portfolio, defining consumption units aligned with underlying costs. Partner with finance on utilization and margin objectives, build business cases, and create frameworks for when to use owned vs. third-party infrastructure.\n, • Investor and Board Engagement: Provide clear updates on strategy, roadmap execution, and adoption using measurable indicators. Partner with the CEO and finance on investor presentations, communicating risks and mitigation plans, and supporting fundraising discussions.\n, • Product Organization and Operating Model: Build, lead, and scale a high-performing product organization across cloud, GPU infrastructure, and inference. Establish clear ownership across strategy, technical PM, and partner products, implement metrics and governance frameworks, and build a culture of customer understanding and commercial accountability.\n\n \n Required Candidate Background \n \n\n • Significant product leadership experience at a public cloud provider, GPU cloud, neo-cloud, AI inference provider, ML platform, or tokens-as-a-service business, with prior ownership of a comparable portfolio and proven success scaling infrastructure or usage-based products.\n, • Demonstrated ability to translate customer/revenue forecasts into compute capacity and deployment plans, with experience working directly with GPU manufacturers, OEMs, and model providers.\n, • Strong understanding of GPU systems and inference runtimes, including how architecture, precision, and parallelism affect performance and cost.\n, • Experience developing pricing, metering, and commitment-based models, and understanding of token economics and cost-per-token optimization.\n, • Experience engaging enterprise customers, investors, and board members, and proven success scaling product management teams.\n, • Strong executive judgment; ability to work regularly from AI Fabrik's Redwood City office and travel as required.\n\n \n Preferred Qualifications \n \n\n • Experience at a hyperscale cloud provider, leading GPU cloud, inference platform, or foundation-model company, with direct experience developing tokens-as-a-service or model-as-a-service offerings.\n, • Experience commercializing products across owned and third-party infrastructure and participating in data center capacity or capital planning.\n, • Familiarity with Kubernetes, model repositories, inference APIs, and consumption metering, and experience with regulated or latency-sensitive workloads.\n, • Strong network within the AI infrastructure, semiconductor, cloud, or model-provider ecosystem. Bachelor's degree or equivalent practical experience.\n\n \n Success in This Role \n \n During the first 12–18 months, the SVP of Product Management will be expected to establish a clear product vision and differentiated market position; create an integrated roadmap connecting infrastructure, GPU capacity, and customer delivery; build a scalable product organization; define the commercial structure for reserved/on-demand GPUs and tokens as a service; establish a customer-discovery process and site-expansion framework; create a GPU OEM and model-provider strategy; launch new services with coordinated cross-functional plans; and implement metrics and credible communications for investors and the board. \n \n Work Location and Travel \n \n This position is based in Redwood City, California and requires regular in-person collaboration with AI Fabrik's executive, product, software, and infrastructure teams, with travel for customer engagements, data center planning, partner meetings, and investor meetings. \n \n About AI Fabrik \n \n AI Fabrik builds an edge inference delivery network for high-performance tokens, with faster time-to-market from grid to tokens. Our mission is to build the inference infrastructure we wished every enterprise already had — close to users, close to the cloud, and extremely resilient for real-time workloads. We are builders, architects, engineers, and researchers with hands-on experience in real-world AI deployment in production, and decades of data center experience that taught us exactly what needs to change. \n \n AI Fabrik was incubated inside Gruve and backed by Mayfield, Xora (Temasek), Acclimate Ventures, Cisco Investments — existing investors from Gruve who followed us into this new chapter. We are deploying five initial production sites, with the first one coming online in July 2026. \n \n Why AI Fabrik \n \n At AI Fabrik, we hire for impact. We want those who challenge how inference infrastructure is built and who excel at delivering it in production. We are builders, architects, engineers, and researchers. We move fast, work with rigor, and care deeply about what runs in the real world. \n \n We are committed to building a diverse and inclusive team. AI Fabrik is an equal opportunity employer. We welcome applicants from all backgrounds and thank all who apply; however, only those selected for an interview will be contacted. \n \n Salary Range \n $300,000-$400,000 + Equity + Bonus