Senior Backend Engineer, Architecture Engineering: Nonlinear Productivity

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. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. *Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role 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 — the kind of output you can actually trust with real engineering decisions. - Build and maintain evaluation tools that judge agent output on correctness, constraint compliance, and cost, not on whether it merely "seems to work." - Work across GitLab's codebase as each problem requires, going wherever the friction actually is rather than staying inside one service or product area. - Apply distributed systems judgment to identify generated code that may fail under concurrency, at scale, or across self-managed, dedicated, and multi-tenant deployments, catching failures before they reach customers. - 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, with your scope and impact free to grow as the team scales. 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, not treated as one-off prompts or demonstrations. - 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. About the team Nonlinear Productivity — shortened internally to "NLP," with no relation to natural language processing — is one of GitLab's newest teams: a strategic incubation group that reports into AI Platform leadership under the direct sponsorship of the CTO. It's split into a US group (this role) and an India-based group working the same charter; the two sync on roadmap and tooling a few times a week but otherwise run day to day on their own. It operates like a startup — no dedicated product manager, no pre-set backlog — and solutions that pr

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.

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 — the kind of output you can actually trust with real engineering decisions. Build and maintain evaluation tools that judge agent output on correctness, constraint compliance, and cost, not on whether it merely "seems to work." Work across GitLab's codebase as each problem requires, going wherever the friction actually is rather than staying inside one service or product area. Apply distributed systems judgment to identify generated code that may fail under concurrency, at scale, or across self-managed, dedicated, and multi-tenant deployments, catching failures before they reach customers. 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, with your scope and impact free to grow as the team scales.