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Senior Product Manager (AI Cloud)
SambaNova Systems · San Jose, United States
About The Role
Join our team as a Senior Product Manager (AI Cloud) and play a key role in defining the strategy for our AI Cloud, which is the strategic engine driving our hardware dominance and enabling our entire business. You will own the roadmap, model strategy, technical definition, value proposition, tokenomics & pricing, and competitive intelligence. This position offers flexible PTO, parental leave, benefits, gym access, and flexible working hours.
- Define the AI Cloud vision to serve as the enabling vehicle for core GTM motions.
- Drive the end-to-end cloud roadmap towards a cohesive strategy, prioritizing features that solve enterprise inference challenges.
- Master the unit economics of the cloud, owning the cost/margin analysis and pricing strategy.
- Strategic Architect: You understand how a cloud product enables hardware sales. You see the link between API features and deal velocity
- AI Native: Deep understanding of the inference market, LLM serving challenges, and the compute provider landscape
- 8+ Years Experience: Product ownership in Cloud Infrastructure, AI/ML, or Compute Platforms
- Technical & Analytical: You can write a technical PRD, model unit economics, and debate strategy with product and engineering leadership
- Inference Market Expertise: Intimate knowledge of the inference landscape—including hyperscalers and specialized providers—and how to effectively position against them
- Hardware-Software Fluency: Deep understanding of AI accelerators and how hardware architecture dictates inference performance
- Bottleneck Analysis: Technical ability to identify critical value blockers in AI inference (latency, throughput, cost) and how our cloud solves them
- Enterprise Strategy: Clear vision for enterprise AI adoption, understanding the specific requirements for security, scalability, and managed services
- Economic Rigor: Capability to model complex cloud unit economics, including tokenomics, margin analysis, and the P&L impact of optimizations
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