Staff Backend Engineer (AI), Verify

GitLab is the intelligent orchestration platform for DevSecOps, enabling organizations to enhance developer productivity and accelerate digital transformation. As a Staff Backend Engineer (AI) in the Verify stage, you will shape the core infrastructure behind GitLab CI, integrating AI into CI/CD workflows to improve performance and usability for users running millions of CI jobs.

Responsibilities:

• 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

Requirements:

• 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