Backend Engineer; Ruby on Rails​/AI), Verify

Staff Backend Engineer (Ruby on Rails/AI), Verify

Remote, APAC;
Remote, Canada;
Remote, Ireland;
Remote, Netherlands;
Remote, United Kingdom;
Remote, US;
Remote, US-Southeast

Git Lab is the intelligent orchestration platform for Dev Sec Ops . Git Lab 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 Git Lab 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. Git Lab 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.

As a Staff Backend Engineer (AI) in the Verify stage at Git Lab, you'll help shape and scale the core infrastructure behind Git Lab 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 Git Lab's Duo Agent Platform into CI workflows at scale, on a foundation that's fast, reliable, secure, and observable.

• 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 Git Lab 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 Git Lab 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.

• Shape and scale Git Lab 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 Git Lab'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…