AI Agent Engineering Lead - Developer Platform & Engineering Productivity

🎯 Your main goal will be to keep n8n operating as an AI-native engineering organization where agents are embedded across the development cycle, helping teams plan, build, test, review, learn, and improve.

You’ll maintain and evolve the platform foundations, tools, guardrails, and team processes that make agentic development safe, measurable, and sustainable. Working with Developer Platform, Security, AI/Agents, Architecture, and product engineering teams, you’ll help teams continuously adopt what works from frontier AI and make success visible through faster feedback loops, better quality, stronger resilience, and more learning from what we ship:

BUILD THE PAVED ROAD FOR AGENTIC DEVELOPMENT

• Define safe task categories for autonomous agents, including what they can attempt, what they should never touch, and where additional review is required.
• Design and implement isolated execution environments where agents can work without exposing secrets or accessing sensitive systems unnecessarily.
• Build pilot workflows that allow selected Linear issues to be picked up by agents, tested, and converted into useful draft PRs.

MAKE N8N’S MONOREPO AGENT-READY

• Create and maintain repo instructions, agent playbooks, ownership metadata, coding guidelines, and documentation that improve agent performance.
• Continuously improve developer workflows, repo structure, testability, and documentation based on where agents succeed, fail, or create reviewer friction.
• Partner with Architecture and senior engineers to define boundaries, dependencies, and areas where agents need more context or tighter constraints.

INTEGRATE AGENTS INTO ENGINEERING WORKFLOWS

• Integrate agentic workflows with GitHub, CI, test infrastructure, and developer review processes.
• Design PR evidence templates that help reviewers understand what changed, why, what tests ran, what risks remain, and where human judgment is needed.
• Partner with Security, AI/Agents, Developer Platform, and product engineering teams to define guardrails for sensitive areas like auth, billing, permissions, and data handling.

MEASURE QUALITY, SAFETY, AND ADOPTION

• Create benchmarks from real historical engineering tasks to evaluate agent output quality, failure modes, test pass rates, review burden, and production risk.
• Define quality signals and adoption metrics such as draft PR usefulness rate, reviewer acceptance rate, CI pass rate, rework rate, and developer satisfaction.
• Use data and feedback from pilot teams to improve agent workflows, reduce unsafe attempts, and build trust across engineering.

REQUIREMENTS

Must-haves

• 🧠 Autonomous coding agents: You have experience with autonomous coding agents, AI coding platforms, agent orchestration systems, or internal AI developer tooling.
• 🤖 Agentic development experience: You have practical experience building, deploying, or operating AI-assisted or agentic software development workflows that work with developers, not besides them.
• 🛠️ Strong engineering foundations: You have a strong software engineering background, ideally in platform engineering, developer experience, infrastructure, tooling, CI/CD, or engineering productivity.
• 🧭 Ambiguity leadership: You’re comfortable leading a first-of-a-kind technical initiative from problem definition through pilot, adoption, measurement, and iteration.
• 🔁 Modern engineering workflows: You deeply understand GitHub, CI, testing strategies, code review, monorepos, issue tracking, developer environments, and release risk.
• 🔐 Safety-first judgment: You have strong judgment around permissions, secrets, sandboxing, sensitive code paths, and human-in-the-loop review.
• 📊 Measurable systems thinking: You know how to design benchmarks, quality signals, failure-mode tracking, and adoption metrics.
• ✍️ Clear written communication: You’re strong at writing repo instructions, playbooks, PR evidence templates, technical proposals, and alignment documents.
• 🤝 Cross-functional influence: You have credibility with senior engineers and can influence across Platform, Product Engineering, AI, Security, and Architecture without formal authority.

Nice-to-haves

• 📦 TypeScript monorepos: You have worked in a large TypeScript, Node.js, or JavaScript monorepo.
• ⚙️ Developer platforms: You have experience with GitHub Actions, Linear, automated testing infrastructure, preview environments, or internal developer platforms.
• 🛡️ Security-aware platform engineering: You have worked on sandboxing, secrets management, least-privilege access, or sensitive code ownership.
• 📏 AI evaluation: You have created evaluation frameworks, benchmarks, or quality metrics for AI-generated code or automation systems.
• 🌍 Open-source communities: You have experience contributing to open-source or source-available projects with active developer communities.
• 🏢 B2B SaaS context: You understand environments where reliability, permissions, data handling, and customer trust are central.
• 🔗 Workflow automation: You’re familiar with workflow automation, integration platforms, AI orchestration, or developer-facing products.

Why join us?

At n8n, you’ll help define how autonomous AI agents become a trusted part of engineering inside one of Europe’s fastest-growing AI-native infrastructure companies. Your work will shape the platform foundations, safety model, evaluation framework, and developer workflows that determine whether agents can contribute meaningfully to production engineering without lowering the quality bar.

You’ll join a remote-first team with high autonomy, high visibility, and a strong builder mindset. Because this is a first-of-a-kind role, you’ll have the opportunity to set direction, influence engineering culture, and create learnings that may inform both internal engineering productivity and n8n’s external product thinking.

Sound like a challenge you’re excited to take on?

Apply now — and help us build the future of automation.