Senior Backend Engineer (Ruby), AI Engineering: AI Coding

Job description

About this role

GitLab is the intelligent orchestration platform for DevSecOps. GitLab 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 GitLab to ship better, more secure software faster.

Responsibilities

• - Design and build the trigger system that links GitLab events, including code pushes, merge request and issue changes, CI/CD results, and deployments, and third-party events from tools like Jira, Jenkins, and Slack to the AI-powered Duo Flows that act on them.
• - Contribute to the broader event platform, the core infrastructure that many of GitLab's AI features rely on to know when and how to run.
• - Architect for high availability and throughput, since this part of the system sits at the center of automated AI workflows across GitLab and needs to reliably process events at scale.
• - Work within GitLab's Rails monolith, where much of the event and trigger system lives, while staying open to other languages used elsewhere in the platform.
• - Influence product and architecture direction by proposing iterative solutions to complex technical problems across a multi-service environment.
• - Collaborate closely with your teammates, product manager, and designer to shape what the trigger system supports next, likely starting with an additional trigger type.
• - Partner cross-departmentally with other Duo Agent Platform teams to make sure triggers and events integrate cleanly with the broader agent ecosystem GitLab is building.
• - Participate in Tier 2 and Tier 3 on-call rotations to troubleshoot production, security, and urgent engineering issues in a globally distributed environment.

Qualifications and requirements

• - Deep experience with Ruby and Rails, comfortable working within a large, established monolith and shipping production code effectively.
• - Proven track record building products that interact with large language models, not training or building machine learning models yourself, but with hands-on experience integrating large language models into real, user-facing tools.
• - Proven track record designing and operating systems built for large scale, with a focus on high availability and throughput in production environments.
• - Hands-on experience with agentic systems and development, including building workflows that let AI agents take autonomous action.
• - Demonstrated ability to make progress in evolving problem spaces and learn new AI capabilities quickly as the underlying technology shifts fast and this team ships while it learns.
• - Strong written communication skills for working asynchronously in a remote, globally distributed team.
• - Additional helpful experience includes Python, since other parts of GitLab's AI platform are built in it, and using GitLab as a product.