Senior Backend Engineer, Architecture Engineering: Nonlinear Productivity

GitLab GitLab · Enterprise · Canada +1 · Architecture Engineering

Senior Backend Engineer on the Nonlinear Productivity team, focused on identifying and removing friction in the software development lifecycle using AI-powered automation and agentic solutions. The role involves designing and building reliable AI systems that use tools, safety checks, and error correction, and developing evaluation tools for agent output. Requires strong distributed systems knowledge and proficiency in Go, Rust, or Python.

What you'd actually do

  1. 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.
  2. 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.
  3. Build and maintain evaluation tools that judge agent output on correctness, constraint compliance, and cost, not on whether it merely "seems to work."
  4. Work across GitLab's codebase as each problem requires, going wherever the friction actually is rather than staying inside one service or product area.
  5. 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.

Skills

Required

  • 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.

Nice to have

  • 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
  • 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.

What the JD emphasized

  • reliable AI-powered automation
  • agentic solutions
  • reliable AI-powered systems
  • step-by-step workflows
  • use tools and safety checks
  • correct errors
  • evaluation tools
  • agent output
  • correctness, constraint compliance, and cost
  • unfamiliar codebases
  • distributed systems knowledge
  • autonomous agents

Other signals

  • AI-powered automation
  • agentic solutions
  • AI-powered systems
  • autonomous agents