Distinguished Engineer, Core DevOps

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. About the Role We are looking for a Distinguished Engineer to lead technical direction across GitLab's Core DevOps services, the systems the majority of our users touch every day. Distinguished Engineers are recognized experts across multiple technology domains and represent the most senior level of technical leadership within and across divisions at the company. Core DevOps builds continuous Integration, continuous deployment, planning, and source control experiences. These are among the most mature, highest traffic, and most deeply interconnected systems at GitLab. They carry years of accumulated architectural decisions, they sit in the critical path of our customers' own delivery pipelines, and they are being reshaped by the shift toward AI native development. The job is to raise their quality, converge them onto a coherent go-forward architecture, and retire accumulated risk, all without interrupting the millions of users and Fortune 100 organizations who depend on them. GitLab is pursuing a set of company-wide engineering goals: large gains in developer productivity, measurably higher quality in what we ship, and engineering practices reshaped around AI and agentic workflows. Core DevOps is where much of that strategy has to land, because these are the services our own engineers and our customers depend on daily. In this role you will bring that strategy into Core DevOps in a form that works for long lived production systems, and carry what you learn here back out to the rest of engineering. What You'll Do Set and continuously refine the go forward technical direction for CI, CD, Plan, and source code experiences, aligned against the horizontal architectural initiatives. Work directly with the VP of Engineering as her technical counterpart on planning, technical investment, and where the group's engineering effort should go. Translate GitLab's company-wide productivity, quality, and AI native engineering goals into concrete technical direction for Core DevOps, and turn into iterable roadmaps, partnering broadly across the org. Define what good means in measurable terms for these services, including availability, latency at p95 and p99, pipeline success rate, test reliability, data correctness, and defect escape rate, then hold designs and implementations to it. Maintain a clear and current picture of systemic technical risk across the group, including single points of failure, unowned critical paths, brittle coupling between CI/CD and Plan, scaling limits, and past decisions that now constrain the roadmap. Drive a prioritized plan to retire it. Lead the hardest design decisions hands on, writing code, prototypes, and proofs of concept where a design argument needs evidence behind it. Drive convergence on shared patterns, libraries, and paved paths, reducing how many different ways the same problem gets solved across the group. Design migration paths that let large, long lived systems change underneath users without breaking them, using incremental cutover, dual run, measurable rollback, and deprecation with clear timelines. Adopt the platform capabilities coming from GitLab's AI o