Remote Senior Product Manager - GPU Products & AI Infrastructure

• GPU Strategy & Vision: Define the product strategy, vision, and roadmap for next-generation GPU instances, high-performance clusters, and cloud services. [

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• Workload Architecture: Translate complex AI, HPC, graphics, and accelerated-computing workloads into rigid product specifications, performance requirements, and technical architectures.

• Lifecycle & Investment: Guide the evolution of GPU infrastructure and make data-driven decisions around platform investments, resource management, and lifecycle management from concept to end-of-life.

• Cloud Economics: Develop advanced business cases, financial models, pricing strategies, profitability analyses, and TCO models to aggressively support product investments.

• Ecosystem Partnership: Partner directly with primary GPU technology and ecosystem providers to align roadmaps, integrations, and bleeding-edge technical requirements.

• Go-To-Market Execution: Develop and execute comprehensive go-to-market strategies, including product messaging, positioning, launch plans, and customer engagement alongside sales and solutions engineering.

• User Advocacy: Represent the direct needs of enterprise customers, engineers, and data scientists by identifying opportunities to improve automation, orchestration, monitoring, and usability.

To land this role, you must possess a rare blend of deep hardware-level intelligence and hyperscale product management acumen. We are filtering for candidates who meet the following high-bar criteria:

• Professional Experience: 12+ years of relevant product management, technology, or engineering experience in massive-scale cloud or hardware ecosystems.

• Target Domain Expertise: Direct, hands-on experience managing GPU cloud infrastructure or accelerated computing products.

• AI & Accelerated Computing: Strong technical understanding of GPU architectures (e.g., NVIDIA, AMD), CUDA, and accelerated computing platforms.

• Cluster Orchestration: Deep experience with AI/HPC workloads and GPU cluster orchestration (including resource management, fabric, interconnects like NVLink/InfiniBand, and large-scale GPU deployments).

• Financial Mastery: Proven capability in developing complex business and financial frameworks for infrastructure, including pricing, TCO, or profitability models.

• Advanced Infrastructure Literacy: Deep knowledge of AI workload patterns, enterprise security requirements, and hardware-level APIs related to GPU infrastructure.

• Execution & Leadership: A strong customer-first mindset with a hyper-focus on automation, usability, and low-friction integration for data scientists. Exceptional ability to gain buy-in from highly technical engineering teams.

• Education: Bachelor’s degree in Computer Science, Engineering, or equivalent deeply technical practical experience

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