Engineering Manager, Trusted Agentic Development

An overview of this role

The Trusted Agentic Development team sits within GitLab’s Developer Experience group and builds the platforms our own engineers rely on every day. GitLab engineers increasingly build software with artificial intelligence (AI) agents. Agents generate code and tests in seconds, so speed is no longer the constraint. Confidence is. Agents can produce a passing test suite for software that doesn’t work as intended, and a reviewer may not be able to tell the difference without redoing the work by hand.

As Engineering Manager, Trusted Agentic Development, you’ll build and lead the team that closes this gap. You’ll help define what validation means when the author is an agent and make the feedback loop fast, automatic, and trustworthy. You’ll point the team at the right version of an open problem and earn adoption across GitLab by building the best path, not by mandating a process.

• Validation that confirms agent-generated code does what was intended, so a passing suite means a working feature and not just a satisfied proxy.
• Mid-loop feedback that helps an agent self-correct toward working software instead of green assertions.
• Language-aware approaches under one shared model of what “trusted” means, starting with Rust, Ruby, and Go, while allowing the right technique to differ by language.
• Real measurement of whether engineers trust and adopt the path, with findings fed directly back into the platform.

What you’ll do

• Build and grow the team by hiring and developing engineers who practice agentic development, and create a culture that treats this as an open problem to solve rather than a process to enforce.
• Set direction in an undefined space by partnering with your team to shape the technical approach for validating agent-built software.
• Keep the team focused on the highest-impact version of the problem rather than the easiest one.
• Earn adoption by building relationships with engineering teams across GitLab, learning how they work with agents today, and creating a platform they choose because it makes them faster and more confident.
• Measure engineer adoption and trust through real usage, treat adopters as design partners, and use findings to prioritize where confidence is breaking down.
• Stay close to the technical work by engaging in architecture and design discussions, reviewing proposals, and keeping your judgment current as agentic development changes.
• Run the team with clear goals, direct feedback, honest performance management, and ownership of complex work from planning through completion without relying on a dedicated program manager.

What you’ll bring

• Direct experience using AI agents to build software, including lessons from successes and missteps and a clear point of view on why a passing test suite isn’t the same as working software.
• Experience leading engineering teams through ambiguous, early-stage problems where the right approach wasn’t yet known.
• The ability to drive adoption through influence and a useful product rather than through mandate, including partnering across teams you don’t manage.
• Technical depth to engage credibly in architecture and validation strategy discussions and earn the trust of senior engineers.
• A history of building accountable, high-performing teams, including hiring well, developing team members, and addressing underperformance directly.
• Strong written communication skills in an all-remote, asynchronous environment.
• Alignment with GitLab’s values, especially collaboration, iteration, and transparency.

This isn’t a quality assurance (QA) or test automation management role. We care far more about your experience leading teams that build with agents than about depth in any particular testing discipline.

About the team

The Trusted Agentic Development team is part of GitLab’s Developer Experience group. We build platforms and practices that help GitLab engineers validate agent-generated software with confidence. We work across engineering teams and programming languages to understand how people use agents, test new approaches in real workflows, and create a shared model of trusted agentic development that teams choose to adopt.