Full Stack Product Manager - Internal AI Applications

About Crunchbase

Crunchbase is a predictive solution that provides intelligence on private companies, powered by the unique combination of live private company data, AI, and market activity from over 80 million users. We predict private market movements that matter to help investors, dealmakers, and analysts make the right decisions.

We are committed to fostering a positive, diverse, and inclusive culture by hiring for potential and embracing individuals with diverse perspectives, backgrounds, experiences, and skill sets. We value transparency and openness, believing that an inclusive environment strengthens our teams and enhances our products.

Crunchbase has a remote-first approach, and is open to hiring in residents of these states: California, Colorado, Illinois, Florida, Georgia, Massachusetts, Missouri, Nevada, New Jersey, New York, North Carolina, Oregon, Pennsylvania, South Carolina, Texas, Virginia, Washington, and Maine.

Our inclusive remote-first culture, competitive pay, and employee wellness benefits set us apart!

About the Role:

As a highly technical Full Stack Product Manager - Internal AI Applications, you will design, build, and operate internal AI-powered systems that improve operational efficiency and reduce manual work across the organization.

This is a hands-on builder role responsible for owning the end-to-end lifecycle of internal AI systems from problem definition and architecture design to implementation, deployment, and ongoing optimization. The role combines strong product management discipline with technical execution, requiring the ability to scope problems, prioritize effectively, and ship reliable, maintainable systems that drive measurable business impact.

This is not a roadmap-only Product Management role. The ideal candidate is comfortable building production-grade solutions and operating independently across both product and technical responsibilities.

What You'll Do:

Product Management & Solution Ownership:

Define business problems, success metrics, and operational requirements for internal AI systems

Prioritize initiatives based on impact, complexity, and business value

Translate ambiguous operational needs into structured requirements and scalable solutions

Break initiatives into iterative milestones and manage delivery from concept through deployment

Track system adoption, operational impact, and ongoing performance metrics

Make tradeoff decisions across speed, quality, scalability, and cost

AI Systems Design & Development:

Design, build, deploy, and maintain internal AI-powered tools and workflows

Develop systems that integrate AI models, data sources, automation, and operational workflows

Implement AI-driven capabilities including prompting, retrieval, and workflow automation

Ensure systems are re