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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 a Staff Backend Engineer (AI) in the Verify stage at GitLab, you'll help shape and scale the core infrastructure behind GitLab CI. You'll play a central role in how we integrate AI into CI/CD workflows. Your work will impact performance, reliability, and usability for people running millions of CI jobs, from small teams to the largest enterprises.
AI is a top priority in the year ahead. In this role, you'll go beyond using AI tools and help define how we design, build, and iterate on AI-assisted and agentic CI experiences. You'll set standards for what good looks like across our AI agent portfolio, including how we measure success, how we instrument behavior in production, and how we account for large language model limitations. You'll also help responsibly integrate GitLab's Duo Agent Platform into CI workflows at scale, on a foundation that's fast, reliable, secure, and observable.
We have ambitious goals for Agentic CI in FY27 . As a Staff Engineer, you will:
- Partner with Engineering, Product, and UX leadership to pressure-test our priorities: where we can move faster, where we're missing data, and where there's whitespace to innovate. Part of this includes learning and growing with the Engineering team you will collaborate closely with.
- Define what success looks like across our agent portfolio and make sure we're tracking against it — not just shipping, but learning.
- Bring a sharp eye to the competitive landscape, helping us understand what it takes to keep GitLab CI best-in-class in an increasingly agentic world.
- Examples of Agentic CI work we have planned for the upcoming year:
- AI Pipeline Builder , the foundational CI agent that auto-creates pipelines for new projects and serves as the launchpad for onboarding new CI users.
- Automate the Fix a Failing Pipeline flow at scale – from dogfooding on internal GitLab projects through to safe, controlled rollout for customers, solving real infrastructure and scalability challenges.
- Build the instrumentation and observability layer that makes agentic CI trustworthy — trigger volume dashboards, retry rates, cost safeguards — so we can measure what's working, catch what isn't, and iterate with confidence.
- Harden the CI pipeline execution infrastructure that these agents depend on: database access patterns, background processing, and job orchestration built to handle the additional load that AI-driven automation introduces at enterprise scale.
What you’ll do
- Shape and scale GitLab CI backend infrastructure to improve performance, reliability, and usability for users running jobs at high volume.
- Design and implement AI-powered features for Agentic CI, including agents, agentic flows, and LLM-backed tooling that integrates with GitLab's Duo Agent Platform.
- Define what success looks like for AI in CI before you build, including baselines, measurable outcomes, and clear signals that help the team learn and iterate.
- Build the instrumentation and observability needed to make AI-assisted CI trustworthy in production, including feature behavior metrics, dashboards, and safeguards.
- Own and drive measurable performance improvements across CI systems (for example, database access patterns, background processing, and job orchestration) by forming hypotheses, running experiments, and validating results with data.
- Write secure, well-tested, maintainable Ruby on Rails code in a large monolith, improving existing features while reducing technical debt and operational risk.
- Lead cross-functional technical work with Product, UX, and Infrastructure, influencing architecture and execution across the Verify stage.
- Share standards, patterns, and learnings with other engineers, raising the bar for responsible AI integration and evidence-driven engineering across CI.
What you’ll bring
- Advanced proficiency with Ruby and Ruby on Rails, with experience building and maintaining reliable backend services in a large codebase.
- Strong PostgreSQL skills, including data modeling, query tuning, and scaling large tables through proactive performance investigation and remediation.
- Hands-on experience building, running, and debugging high-traffic production systems, ideally in CI, workflow orchestration, or adjacent infrastructure-heavy domains.
- Practical experience designing and shipping AI-powered backend features and integrations, including sound judgment about large language model limitations and responsible use in production.
- A data-driven approach to engineering: defining hypotheses, establishing baseline metrics, instrumenting changes, and measuring outcomes against clear success criteria.
- Familiarity with observability patterns and tools (metrics, logging, tracing) to diagnose issues, improve reliability, and guide iteration.
- Strong backend architecture and delivery practices, including secure design, well-tested code, and strategies for safe rollouts and zero-downtime changes.
- Clear written and verbal communication skills, including writing technical proposals and documentation, and collaborating effectively in a remote, asynchronous, cross-functional environment.
About the team
The Verify stage focuses on collaboration, iteration, and helping GitLab users run fast, reliable, and scalable Continuous Integration (CI) pipelines for projects of all sizes, from small teams to large enterprises. For more on how we work, see Team Handbook Page and Related Initiative.
Remote-Global
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GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .
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