Director of Engineering, Security Factory

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As Director of Engineering, Security Factory , you will lead the engineering organization responsible for GitLab’s customer-facing security capabilities across our AI-powered DevSecOps platform. You’ll help shape how customers detect, prioritize, and remediate vulnerabilities across modern software workflows by guiding teams that build proprietary scanners, AI-driven detection engines, agentic remediation flows, and supporting security foundations. Reporting to the VP of Engineering, Sec section , you’ll set the engineering vision and roadmap for a distributed group of nine teams, including engineering managers, and create the conditions for strong delivery, sound technical direction, and healthy team growth. Some examples of our projects: Advanced proprietary scanners for Static Application Security Testing, Software Composition Analysis, Secret Detection, and related AI and machine learning detection engines Agentic security flows for autofix and guided remediation, plus research that strengthens AI security, threat intelligence, vulnerability management, and security foundations What you’ll do Set the engineering vision and multi-quarter roadmap across teams working on proprietary scanners, AI-driven security workflows, research functions, vulnerability management, and security foundations. Lead a distributed engineering organization of managers and individual contributors, with a focus on team performance, engagement, and career development. Drive architectural decisions for AI and machine learning detection engines, agentic remediation flows, and scalable scanning infrastructure. Partner with product management to define priorities, shape requirements, and deliver security capabilities for customers in regulated and security-conscious environments. Own the engineering delivery of GitLab’s proprietary application security scanners, agentic remediation workflows, and AI Security Research efforts. Represent the Security Factory stage in cross-functional planning, executive reviews, security disclosures, and customer conversations. Establish engineering standards for delivery, observability, incident response, scanner quality, and code quality. Contribute to GitLab’s transparent, async-first way of working through issues, merge requests, and the GitLab handbook. What you’ll bring Experience leading engineering organizations with multiple teams and managers in a distributed environment. Strong understanding of application security fundamentals, including Static Application Security Testing, Software Composition Analysis, secret detection, vulnerability management workflows, and software supply chain security. Experience building detection, analysis, or scanning systems in a software as a service or DevSecOps environment, including trade-offs across precision, recall, latency, and scale. Direct experience shipping a customer-facing AI or machine learning product feature tied to detection or remediation quality outcomes. Ability to partner closely with product management on roadmap planning, prioritization, and requirements in a product-led context. Strong writte