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

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Aplicando paraStaff Backend Engineer (Ruby on Rails/AI), VerifyGitLab

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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.

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.

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.

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.