Staff Backend Engineer, Architecture Engineering: Nonlinear Productivity

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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.

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An overview of this role

As a Staff Engineer, you'll be the technical anchor for GitLab's Nonlinear Productivity team in the US: the person who decides what "proven" means before something ships, and who helps shape what the team builds next, not just how to build it. It's a from-scratch, generalist team with no dedicated product manager — that ownership starts on day one.

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

Set the technical direction for the team's agentic systems, from how agents are orchestrated to where a step should stay human-owned, and defend those calls once they're tested against real code.

Discover and prioritize sources of friction across GitLab's SDLC, driving the fix — agentic, process-based, or both — from a rough hypothesis through to a shipped, measured result.

Work across any part of GitLab's codebase as the problem requires, since this team operates like a small, generalist group rather than one scoped to a single service.

Apply distributed systems judgment to catch cases where generated code looks correct but breaks under concurrency, at scale, or across deployment topologies (including self-managed, dedicated, and multi-tenant environments), and coach others to do the same.

Mentor senior and mid-level engineers on agent engineering practices and distributed systems judgment, through design reviews and pairing that raise the team's collective bar rather than just your own output.

Collaborate with the India-based group a few times a week to align on the roadmap, and represent the team's technical progress to stakeholders in the Chief Technology Officer's organization.

Serve as a bar raiser for the team's hiring, owning the Technical Leadership round for other Staff-level candidates as the team scales.

Own a greenfield technical foundation from day one, with your scope and impact free to grow as the team scales.

What you'll bring

Experience building reliable agentic or large language model (LLM)-based systems, including multi-step orchestration, tool use, guardrails, and recovery.

Ability to work autonomously in unfamiliar codebases and drive solutions from discovery through completion.

Strong distributed systems and computer science fundamentals, including coordination, consistency, idempotency, rate limiting, failure modes, and degradation under load.

Proficiency in Go, Rust, or Python, with the ability to read and modify code in the others.

A track record of delivering results from unclear or incomplete requirements — able to take a complex, loosely specified problem and decompose it into a concrete proposal of small, shippable steps.

Experience designing evaluation frameworks for systems where "looks plausible" and "is actually correct" are different questions, and a track record of raising the quality bar for a team's output, not just your own.