Position OverviewWe are seeking a SVP/VP/Director of Product to lead the product strategy, portfolio, commercialization, and team development for our AI cloud infrastructure platform.
Centered on GPU cloud computing, this role will oversee the development of our cloud infrastructure, AI training and inference platforms, and related product capabilities. The VP will be responsible for transforming the company's global data center and GPU resources into a unified, standardized, and scalable AI cloud platform.
The ideal candidate brings experience from a leading public cloud provider, GPU cloud platform, or neocloud company. This individual should combine a strong understanding of cloud and AI infrastructure with the ability to develop product strategy, identify market opportunities, lead product teams, and drive execution across functions.
Key Responsibilities- Define the overall product strategy, product architecture, and long-term roadmap for the company's AI cloud platform, including market positioning, core product offerings, and investment priorities.
- Lead the product portfolio across GPU compute, bare metal, containers, networking, storage, resource management, and shared cloud platform capabilities.
- Define the product direction for AI training, inference, model deployment, and serverless inference, continuously improving the experience for developers and enterprise customers.
- Own product packaging, business models, pricing, metering, and SLA design, with accountability for product revenue, gross margin, resource utilization, and customer adoption.
- Develop a deep understanding of the needs of AI companies, developers, and large enterprises, and translate customer requirements into standardized and repeatable platform capabilities.
- Partner closely with engineering, infrastructure, operations, sales, and solutions teams to drive the full product lifecycle from strategy and development through launch, adoption, and ongoing optimization.
- Build and lead the AI cloud product organization, establishing effective processes for product decision-making, roadmap planning, requirements management, releases, and product operations.
- Monitor global trends in AI infrastructure, GPU technology, and cloud computing, and represent the company in strategic customer and ecosystem partner engagements.
RequirementsQualifications- 10+ years of experience in cloud computing, infrastructure, or enterprise technology products, including experience leading products or product teams.
- Product experience with a major public cloud, GPU cloud, AI infrastructure platform, or neocloud provider.
- Demonstrated ownership of a major cloud product line, or leadership experience in building, significantly expanding, or commercially scaling a cloud platform.
- Strong understanding of GPU computing, cloud infrastructure, resource scheduling, networking, storage, containers, metering, and billing.
- Familiarity with the major product and infrastructure requirements for large-model training, inference, model serving, and AI workloads.
- Strong product strategy, market judgment, and prioritization skills, with the ability to balance customer needs, technical complexity, investment requirements, and commercial value.
- Experience with product pricing, cost management, infrastructure utilization, and commercialization, with accountability for measurable business outcomes.
- Proven leadership and cross-functional execution skills, including the ability to align product, engineering, infrastructure, sales, and operations teams.
Preferred Qualifications- Experience at a leading public cloud provider such as AWS, Microsoft Azure, Google Cloud, OCI, Alibaba Cloud, or Tencent Cloud.
- Experience at CoreWeave, Lambda, Crusoe, Nebius, or a comparable neocloud or GPU cloud platform.
- Experience managing large-scale GPU capacity with responsibility for revenue, gross margin, or infrastructure utilization.
- Experience with global, multi-data-center, or multi-region cloud platforms.
- Strong knowledge of NVIDIA's GPU roadmap, large-model training and inference frameworks, and the broader AI infrastructure ecosystem.
Key Success Metrics- Establish a clear and differentiated AI cloud product strategy and portfolio.
- Deliver critical GPU cloud infrastructure and AI platform capabilities on schedule and drive customer adoption.
- Improve GPU utilization, platform revenue, product gross margin, and customer retention.
- Convert customer-specific requirements into standardized and scalable products.
- Build a high-performing AI cloud product organization capable of supporting the company's long-term growth.
