Distinguished Engineer, Agentic SDLC & Non‑Linear Productivity

About the Role

We are looking for a Distinguished Engineer to pioneer and scale autonomous, agentic SDLC capabilities across GitLab. Distinguished Engineers are recognized experts across multiple technology domains and represent the most senior level of technical leadership within and across divisions at the company.

In this role you will deeply immerse in GitLab's product and internal engineering workflows to identify classes of problems that can be wholly or largely addressed by AI agents, validate them through rigorous experimentation in production-adjacent environments, and codify patterns that can be productized for our millions of users. You will act as a bridge between Architecture, Product, Infrastructure, and Data and ML teams, ensuring that agentic capabilities deliver durable value internally first and then scale reliably and securely to our customers.

The level for this role will be determined through the interview process and may be scoped as either Distinguished Engineer or Fellow Engineer based on the depth and breadth of experience demonstrated.

What You'll Do

• Define and continuously refine a company-wide technical vision for autonomous, agentic SDLC that aligns with GitLab's product strategy and Engineering job architecture
• Identify and prioritize non-linear productivity opportunities across the SDLC, from planning and coding to review, security, compliance, and operations, targeting 10x step changes rather than incremental gains
• Translate ambiguous problem spaces into concrete, iterable roadmaps in partnership with Product, AI and ML, and Architecture teams
• Lead hands-on experiments and prototypes to validate where agentic workflows can fully own or materially reshape engineering tasks, including autonomous MR authoring, test creation and triage, security remediation, release readiness, and incident response
• Design and implement reference architectures for agentic SDLC inside GitLab, including orchestration patterns, safety guardrails, observability, and human-in-the-loop controls
• Define evaluation frameworks using offline benchmarks and online experiments to measure correctness, latency, safety, cost, and productivity impact of agentic workflows
• Select and own a small set of high-impact internal use cases as pathfinders and drive them from concept through adoption to measurable productivity gains
• Work directly with engineering teams to embed agentic workflows into day-to-day development, ensuring they are trusted, observable, and resilient
• Define and track core productivity metrics such as cycle time, MTTR, and MR throughput, and link agentic interventions to real business outcomes
• Capture and codify reusable patterns, libraries, and playbooks that other teams can adopt with minimal friction
• Work with Product Management and Engineering leadership to convert proven internal patterns into product capabilities that can be safely and reliably offered to customers
• Ensure designs respect multi-tenant, compliance, and data governance requirements across GitLab.com and self-managed customers
• Serve as a point of escalation for complex technical and architectural decisions related to agentic workflows, AI safety, and large-scale systems integration
• Mentor Principal and Staff Engineers working on AI and agentic efforts, raising the overall bar for technical execution, experimentation rigor, and cross-team collaboration
• Write clear, opinionated design documents, architecture narratives, and decision records that help teams make aligned, high-quality decisions independently
• Represent GitLab in the broader ecosystem at conferences, standards groups, and open source communities on topics such as AI-assisted development, autonomous agents, and productivity measurement
• Partner with Security and Compliance to define guardrails, review processes, and monitoring for agentic features, ensuring responsible use of AI and protection of customer data
• Work with Reliability and SRE teams to ensure that agentic services are observable, debuggable, and resilient, and that failure modes degrade gracefully

What You'll Bring

• 10+ years of software engineering experience, including 4+ years in a Staff, Principal, or equivalent senior technical leadership role
• Deep expertise in AI and ML systems, including large language models, agentic frameworks, and autonomous workflow design at production scale
• Proven track record of leading hands-on technical experimentation, including defining evaluation frameworks, running benchmarks, and translating findings into scalable architecture decisions
• Strong background in scalable, multi-tenant distributed systems, including service decomposition, fault tolerance, observability, and operational resilience
• Experience designing and implementing human-in-the-loop controls, safety guardrails, and responsible AI practices for production systems
• Demonstrated ability to drive cross-functional alignment across Engineering, Product, Infrastructure, and Data and ML teams on complex, ambiguous technical challenges
• Experience mentoring senior engineers and influencing technical direction across multiple teams or divisions without direct authority
• Ability to work effectively in a fully remote, globally distributed organization with excellent written and asynchronous communication skills
• Familiarity with GitLab's DevSecOps platform, CI/CD primitives, and the software development lifecycle is a strong plus