Staff Product Manager, Managed Intelligence (sf/sunnyvale)

Crusoe · Data AI · Sunnyvale, CA - US · Product and Design

Product Manager for Crusoe Cloud's Managed Intelligence services, focusing on defining, building, and scaling AI and agentic capabilities. The role involves strategic roadmap execution, market growth, customer advocacy, and defining the model lifecycle from data ingestion to inference and agentic workflows, bridging research and product for AI-native companies.

What you'd actually do

  1. Define and lead the product strategy for Managed Intelligence, focusing on building next-generation AI and agentic capabilities at Crusoe.
  2. Engage cross-functional teams—including Engineering, Research, and GTM—to land and execute strategic roadmaps that drive long-term platform value.
  3. Drive initiatives to grow the business by attracting AI-native companies and developers to the platform through differentiated service offerings.
  4. Champion a customer-centric product culture, ensuring that user feedback and developer needs directly inform the evolution of our AI services.
  5. Establish the product requirements for a seamless model lifecycle, from data ingestion and fine-tuning to high-performance inference and agentic workflows.

Skills

Required

  • 5+ years of dedicated experience in AI/ML product management
  • track record of launching and scaling complex technical services
  • Deep familiarity with modern AI workflows and the end-to-end model lifecycle, including training, orchestration, and deployment
  • A sophisticated understanding of the current AI industry landscape, emerging product gaps, and the evolving needs of AI-native developers
  • Exceptional communication and collaboration skills
  • ability to influence senior stakeholders and align diverse technical teams
  • A proactive, "0 → 1" approach to product development
  • grit to scale services in a fast-paced, high-demand environment

Nice to have

  • Direct experience working with or within a foundational model lab
  • familiarity with emerging techniques such as reinforcement learning (RLHF/PPO/DPO)
  • advanced agentic architectures
  • Active participation in AI developer communities
  • pulse on the open-source ecosystem
  • Experience working at a cloud hyperscaler or specialized AI infrastructure provider

What the JD emphasized

  • next-generation AI and agentic capabilities
  • seamless model lifecycle
  • high-performance inference and agentic workflows
  • AI/ML product management, with a track record of launching and scaling complex technical services
  • end-to-end model lifecycle, including training, orchestration, and deployment
  • evolving needs of AI-native developers
  • 0 → 1 approach to product development with the grit to scale services in a fast-paced, high-demand environment

Other signals

  • defining, building, and scaling AI services
  • creating future AI and agentic capabilities
  • defining the product strategy for Managed Intelligence
  • building next-generation AI and agentic capabilities
  • Establish the product requirements for a seamless model lifecycle, from data ingestion and fine-tuning to high-performance inference and agentic workflows.