Staff Backend Engineer, Architecture Engineering: Nonlinear Productivity

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.
• A history of unblocking and enabling teammates — through design reviews, technical writing, or mentoring — and of engaging regularly with other teams to find where collaboration actually pays off.

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. 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 want technical ownership of something with no existing playbook, and a hand in defining a brand-new part of GitLab from its first commit.