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n8n is an open workflow orchestration platform built for the AI era, with 650K+ active developers, 190K+ GitHub stars, and a $5.2bn valuation. The company is building an AI-native engineering organization where autonomous agents are embedded across the development cycle.
As an Agentic Engineering Platform Engineer, you will lead the design and implementation of safe, measurable, and sustainable agentic development practices within n8n's engineering organization. Your primary goal is to maintain and evolve the platform foundations, tools, guardrails, and team processes that enable agents to help teams plan, build, test, review, learn, and improve.
Key responsibilities include:
**Build the Paved Road for Agentic Development**: Define safe task categories for autonomous agents, specifying 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. 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 Linear, 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.
You will work cross-functionally with Developer Platform, Security, AI/Agents, Architecture, and product engineering teams. This is a first-of-a-kind role with high autonomy and visibility, where you'll help define how autonomous AI agents become a trusted part of engineering at one of Europe's fastest-growing AI-native infrastructure companies.
**Requirements**
Must-haves:
- Experience with autonomous coding agents, AI coding platforms, agent orchestration systems, or internal AI developer tooling
- Practical experience building, deploying, or operating AI-assisted or agentic software development workflows that work with developers, not beside them
- Strong software engineering background, ideally in platform engineering, developer experience, infrastructure, tooling, CI/CD, or engineering productivity
- Comfort leading a first-of-a-kind technical initiative from problem definition through pilot, adoption, measurement, and iteration
- Deep understanding of GitHub, CI, testing strategies, code review, monorepos, issue tracking, developer environments, and release risk
- Strong judgment around permissions, secrets, sandboxing, sensitive code paths, and human-in-the-loop review
- Ability to design benchmarks, quality signals, failure-mode tracking, and adoption metrics
- Strong written communication skills for repo instructions, playbooks, PR evidence templates, technical proposals, and alignment documents
- Credibility with senior engineers and ability to influence across Platform, Product Engineering, AI, Security, and Architecture without formal authority
Nice-to-haves:
- Experience working in large TypeScript, Node.js, or JavaScript monorepos
- Experience with GitHub Actions, Linear, automated testing infrastructure, preview environments, or internal developer platforms
- Background in sandboxing, secrets management, least-privilege access, or sensitive code ownership
- Experience creating evaluation frameworks, benchmarks, or quality metrics for AI-generated code or automation systems
- Contribution to open-source or source-available projects with active developer communities
- B2B SaaS context understanding where reliability, permissions, data handling, and customer trust are central
- Familiarity with workflow automation, integration platforms, AI orchestration, or developer-facing products
