Senior Backend Engineer, Architecture Engineering: Nonlinear Productivity

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.

• 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.

• 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.

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 prove out internally are the team's path to a monetized, customer-facing GitLab offering.

It's a good fit for engineers who'd rather go find the next problem than be handed one, and who want a hand in defining a brand-new part of GitLab from its first commit.