Lead Product Manager

cyberu•Dublin, CA

1d•$135,400 - $216,000•Hybrid

About The Position

Cornerstone OnDemand is seeking a Lead Product Manager to lead the delivery of AI-native products and enterprise outcome agents that drive measurable customer value. This is a customer-facing AI-native product management role for a hands-on PM who can move quickly from customer discovery and requirements to working prototypes, using modern AI-native product development tools such as Lovable, Claude, Co-Pilot and similar platforms. The role requires close collaboration with customers, FDEs, engineering, and cross-functional teams to validate solutions early, iterate rapidly, and launch outcome agents that solve meaningful business problems. The ideal candidate is highly execution-oriented, comfortable working in ambiguity, and able to translate customer needs into prototypes, product direction, and shipped capabilities with speed and rigor. Preference for hybrid in one of our offices: Dublin, CA or Santa Monica, CA or New York, but may consider other locations.

Requirements

5+ years of experience in product management, including significant experience delivering enterprise software, SaaS, AI, or platform products.
Strong track record in customer-facing product delivery for enterprise SaaS or AI-native products.
Demonstrated ability to go from customer discovery and requirements to working prototypes and launched solutions.
Hands-on experience using AI-native prototyping and product development tools such as Lovable, Claude, Co-Pilot or similar tools to accelerate product definition and validation.
Experience collaborating closely with customers and technical delivery partners such as FDEs to iterate rapidly and launch customer-ready solutions.
Strong product execution skills, including prioritization, prototype validation, release planning, cross-functional coordination, and delivery tracking.
Experience translating ambiguous customer problems into structured product requirements and testable product experiences.
Strong analytical, communication, and stakeholder management skills.
Ability to operate effectively in fast-moving, matrixed environments and drive alignment across technical and business teams.

Nice To Haves

Experience delivering AI agents, copilots, workflow agents, or decision-support products.
Experience in enterprise AI, workforce technology, learning, talent, HR technology, or related SaaS domains.
Demonstrated success pioneering or scaling next-generation learning products, with deep expertise leveraging AI micro-learning generation, automated role-plays, AI avatar workflows, and intelligent digital assessments to drive engagement.
Demonstrated success using rapid prototyping to accelerate product-market fit and customer adoption.
Experience working in close partnership with customer success, services, sales, and solution delivery teams.

Responsibilities

Lead end-to-end delivery of AI-native products and enterprise outcome agents, from customer discovery and requirements gathering through prototype creation, iteration, and launch.
Partner directly with customers to understand business problems, workflows, pain points, and desired outcomes.
Translate customer needs into clear product requirements, use cases, and solution concepts that can quickly be tested and refined.
Build and evolve working prototypes using AI-native tools such as Lovable, Claude, Co-Pilot and similar rapid prototyping platforms to accelerate validation and shorten the path from idea to delivery.
Turn customer specific outcome agents into re-sellable out-of-the box agents
Use prototypes as the new PRD motion: move from customer discovery to working AI Agent prototypes that customers, FDE’s and engineering can review, test, and refine.
Collaborate closely with FDEs and engineering to iterate on prototypes, validate feasibility, refine user flows, and prepare solutions for launch.
Work with customers, FDEs and engineering in tight feedback loops to improve prototypes, confirm value, and shape deliverable product capabilities.
Drive cross-functional execution across product, engineering, design, data science, architecture, and go-to-market teams to bring validated concepts into production.
Ensure launched outcome agents are practical, scalable, and easy for enterprise customers to adopt.
Use customer feedback, adoption signals, and business-impact metrics to continuously improve product execution and outcomes.