Backend Engineer

As a Senior Backend Engineer on GitLab's Nonlinear Productivity team, you'll find and remove friction across the software development lifecycle using reliable AI-powered automation — diagnosing problems like long review cycles, manual release steps, and brittle automation, then building the automation and process changes that resolve them for good. Some examples of the problems this team takes on: - Cutting the time it takes to complete a good code review — one that still keeps a human in the loop — by half, using agentic solutions. - Turning a manual, error-prone release step into an agentic workflow that catches its own mistakes before a human ever has to. What you'll do: - Identify sources of friction across GitLab's software development lifecycle and scope agentic solutions to address them, turning vague pain points into concrete, buildable proposals. - Design and build reliable AI-powered systems that follow step-by-step workflows, use tools and safety checks, and correct errors before taking engineering action. - Build and maintain evaluation tools that judge agent output on correctness, constraint compliance, and cost. - Work across GitLab's codebase as each problem requires. - Apply distributed systems judgment to identify generated code that may fail under concurrency, at scale, or across self-managed, dedicated, and multi-tenant deployments. - Collaborate with the India-based group, sharing roadmaps, findings, and reusable agent tooling. - Take ownership of a greenfield problem space from day one, helping shape a proven internal fix into a capability GitLab could offer customers externally. What you'll bring: - Hands-on experience building agentic or large language model-based systems — multi-step orchestration, tool use, guardrails, and recovery patterns — and making them reliable in production. - A track record of working autonomously in unfamiliar codebases, getting oriented quickly, and driving solutions through completion. - Strong distributed systems knowledge, including coordination, consistency, idempotency, rate limiting, failure modes, and degradation under load. - Proficiency in Go, Rust, or Python, in that order of team priority, and the ability to read and modify code in the other languages. - Helpful experience includes shipping autonomous agents that complete real tasks from start to finish; improving build systems, release processes, review workflows, or other parts of the software development lifecycle; and working with globally distributed teams, large language model workload costs, or production constraints across on-premises, air-gapped, single-tenant, and software-as-a-service deployments.