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

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.