Senior Engineering Manager, AI Developer Experience

About the role

Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer's toolbox, and are building the team that will make that happen. Our values guide us -they are present in how we show up, make decisions, and work together to make an impact. We're curious, driven, collaborative, genuine and humble. Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you! As a Senior Engineering Manager on the AI Developer Experience team, you will lead a team that empowers a company of builders by delivering a frictionless, standardized ecosystem of core engineering and AI tools. You'll operate like internal Forward Deployed Engineers-partnering directly with teams across the company to accelerate safe, effective AI adoption-while also being an early adopter of our own AI products to create a tight feedback loop with product leadership. You will balance speed with reliability: removing invisible barriers that break flow, building paved paths, and prioritizing measurable business impact. Scope & Responsibilities Set direction for AI-assisted development, end-to-end: Define the engineering-wide approach for safe, effective AI-assisted workflows (tooling standards, rollout strategy, and adoption). Build frictionless paved paths: Identify and remove sources of friction in developer workflows; deliver a small set of deeply reliable, well-supported paved roads rather than many bespoke bypasses. Internal AI platform strategy (build vs. buy): Own strategy and execution for internal agent runtimes and developer-facing AI systems (authoring, review, and automation workflows), including vendor evaluation and integrations. Governance, cost, and measurement: Drive governance and cost management for AI tooling (usage measurement, guardrails, optimization) using clear metrics and frameworks (e.g., DORA/SPACE) to translate improvements into business outcomes.