UK remote
Senior AI Product Manager, EMEA
About this role
Company Overview: At Panopto, we are the most customer-centric learning technology company in the world. As the leader in visual and audio-based learning, we empower organizations to share knowledge effortlessly in a capture and post-capture world. We don’t just build software; we obsess over our users’ goals to deliver solutions that truly matter. Our mission is simple: to attract the brightest talent, people like you, to Elevate the Craft and do the most impactful work of your career.
As we continue to support growth and expansion, we are seeking an experienced Senior AI Product Manager whose technical depth and entrepreneurial mindset will drive the next generation of AI-powered capabilities. Position Summary In this role, you will have the opportunity to do the most impactful work of your career, elevating your craft while contributing to a team that values lifelong learning. The Senior AI Product Manager owns strategy, discovery, definition, and delivery for customer-facing AI-powered product capabilities.
This role combines senior product management leadership with hands-on technical validation—using codebase analysis and functional prototypes to give engineering an executable starting point rather than a written specification, while owning the economics, quality, and security of shipped AI features You will serve as a strategic advisor, utilizing structured product frameworks (e.g., Jobs to Be Done (JTBD), Opportunity Solution Trees) while also establishing AI-driven workflows that elevate practices across the broader product team.
At Panopto, we collaborate as a team which may mean taking on additional tasks or projects within the scope of your role. This also offers growth opportunities and helps us scale as business needs evolve. You’ll also have opportunities to contribute to other initiatives that directly advance our core values and support you in elevating your craft. How You’ll Contribute In this role, you will: AI Product Ownership: Own AI product capabilities through discovery and delivery, incorporating model behavior, data requirements, evaluation methods (evals), unit economics (cost-per-task, model selection, caching), quality, security considerations, and gross margin impact into product decisions.