Staff AI Developer Productivity Engineer

• 8+ years in software engineering with a track record of building internal developer tooling/DevEx (CLI/IDE plugins, bots, internal services or web apps)
• Strong proficiency in TypeScript/Node.js, C#/.NET and front-end experience with React/Next.js
• Hands-on LLM integration: prompting, tool/function calling, retrieval (embeddings/vector stores) and basic evaluation of quality
• Solid understanding of CI/CD, test strategy and code-quality tooling; able to design fast inner loops and reliable pipelines
• Product mindset: discovery interviews, hypothesis-driven experiments, before/after evaluation and clear business cases
• Excellent communication and influence skills; comfortable enabling teams via docs, workshops and remote office hours
• A role model for our values:
• Make it happen
• Succeed together
• Be the customer
• Go beyond

Desirable

• This is a staff-level, hands-on role at the intersection of Developer Productivity, Developer Experience (DevEx) and AI
• You’ll accelerate a 100+ engineer organisation by identifying bottlenecks across the developer lifecycle and build and ship internal tools that measurably improve delivery speed and quality
• You’ll operate with an AI-first mindset: rapidly experiment with cutting-edge AI technologies and agentic flows to see what’s possible today, map the frontier and forecast capabilities likely to become viable in the next 3-6 months
• When AI isn’t the right fit, you’ll pragmatically fall back to conventional solutions
• You’ll self-manage discovery, roadmap and backlog; run short, data-driven trials and productionise solutions which work
• You’ll partner closely with Engineering and DevOps teams to ensure changes integrate cleanly into existing workflows
• AI-first discovery roadmap – Interview engineers, analyse telemetry and prioritise the highest-leverage opportunities across coding, review, testing, releases and knowledge flows
• Set baselines and target deltas and maintain a transparent backlog/roadmap grounded in measurable outcomes
• Ship internal tools – Build and maintain AI-augmented dev tools across IDE/CLI, GitHub, Slack, Notion, Retool and internal web apps that reduce manual effort and cycle time
• Agentic flows across the SDLC – Design structured, agentic workflows (including MCP and tool-use with human-in-the-loop gates) to improve code authoring/review, test creation/selection and the release, incident response and onboarding processes
• Frontier scouting & evaluation – Continuously experiment with cutting-edge AI models, frameworks and runtimes; run short, hypothesis-driven pilots with clear success criteria and developer-fit assessments; productionise successful approaches
• Automation pipelines – Implement LLM-assisted and AST-based code-mod workflows for migrations, boilerplate generation, test creation and docs updates
• Enablement & change management – Drive adoption with playbooks, golden prompts, demos, office hours and a champions network across build teams; measure usage and satisfaction and iterate
• Measurable impact – Reduced cycle time for new features
• Quality & reliability – Improved stability; fewer defects and incidents attributable to code changes
• Adoption & enablement – Growth in weekly active users of AI and other dev tools; strong satisfaction with guidance, prompts and enablement materials
• Developer satisfaction – Higher engineer NPS and engagement
• Operational excellence – Well-maintained internal tools with monitoring, docs and responsive support; clear deprecation paths for experiments that don’t meet the bar