Senior Product Manager, AI-Native Security

At JFrog, we’re reinventing DevOps to help the world’s greatest companies innovate – and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit, and just all-around great people. Here, if you’re willing to do more, your career can take off. And since software plays a central role in everyone’s lives, you’ll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production – a concept we call “liquid software.” Wouldn't it be amazing if you could join us on our journey? We are looking for a Senior Product Manager to join our JFrog Security team, focused on AI-native security experiences. You will work horizontally across JFrog Security products – Xray, Curation, Advanced Security, Frogbot, and our GitHub integration – owning best practices, adoption playbooks, and agentic security integrations. This is a builder role – your output is not just a backlog, but working integrations, playbooks, and frameworks that directly move customer adoption. As a Senior Product Manager at JFrog you will… Design and build JFrog Security MCP integrations – making JFrog queryable by AI coding agents such as Claude, Cursor, and Copilot. Own best practices playbooks for JFrog Security products, published for use by CS, SolEng, and customers. Work closely with CS and SolEng to understand adoption blockers and translate them into product and documentation improvements. Contribute to the agentic security vision – defining how AI agents interact with JFrog Security context across the SDLC. Partner with Engineering and PM peers across Xray, Curation, and Advanced Security to ship cross-product experiences. Stay ahead of the MCP and LLM tooling ecosystem and identify integration opportunities for JFrog. To be a Senior Product Manager at JFrog you need… Hands-on technical background – engineering, platform, or post-sales/solutions engineering Practical experience with AI tooling – building or integrating LLM-based tools (MCP, Cursor plugins, AI agents, or similar) Experience working directly with customers or field teams (CS, SolEng, or pre/post-sales) Strong written communication skills – the ability to write clear, actionable technical playbooks Familiarity with SDLC and DevOps practices Security domain knowledge – as a practitioner, developer who has worked with CVEs, or experience at a security company – an advantage Post-sales or Solutions Engineering background – an advantage