About the teamThis role sits within APEX (AI Foundations), the team building the platform-level infrastructure behind ServiceNow's enterprise AI strategy. You'll work alongside engineering, data, and applied research partners to make evaluation a durable, compounding advantage.About the roleAgentic AI is only as trustworthy as the evaluation discipline behind it. ServiceNow is building the evaluation methodology, tooling, and closed-loop production system that will define how good is good enough for AI specialists and multi-agent frameworks across the enterprise — and we're looking for the product leader to help own that discipline. This is a rare chance to build a category-defining evaluation platform from the ground up, at a company where the outcome shapes how every business unit ships AI, not just one team's roadmap.Why this role mattersEvaluation is the foundational differentiator for enterprise agentic AI. As frontier models commoditize, defensible advantage shifts to the orchestration and quality layer — how reliably an AI specialist performs in a customer's production environment. This role sits at exactly that inflection point:You set direction in undefined space — there is no existing playbook to inherit, and the discipline you build becomes the standard others build on.Your impact is horizontal and enterprise-wide by design, driven through influence, tooling, and a Center of Excellence rather than headcount.You'll be the definitive point of reference for AI specialist quality across the portfolio — deep technical and methodological ownership without the dilution of people management.Success is visible and concrete: your framework adopted across business units, a closed-loop system demonstrably improving specialists release-over-release, and tooling used self-serve by teams you never directly staffed.The impact you'll makeEvaluation strategy and framework: Define the end-to-end evaluation methodology across ServiceNow's AI specialist portfolio — golden datasets, LLM-as-judge calibration, failure taxonomy, and a multi-layer metric model spanning agent behavior, user experience, and business impact.Closed-loop evaluation platform: Move evaluation from a one-time release gate to a continuous improvement engine, where production telemetry feeds failure analysis, targeted evaluation expansion, and redeployment — making live signal, not synthetic testing, the primary driver of quality.Cross-functional influence at scale: Partner with a federated Center of Excellence model — central methodology and tooling, embedded practice in each business unit — acting as the internal consulting function that helps teams stand up evaluation without rebuilding infrastructure from scratch.Platform and tooling: Drive evaluation from bespoke effort to reusable, self-serve platform capability — evaluation infrastructure, data tooling, and calibration systems that make closed-loop evaluation available to every team building AI specialists.What Success Looks LikeYour evaluation framework is adopted across multiple business units.The closed-loop platform demonstrably drives specialist improvement release-over-release, sourced from real production signal.Evaluation tooling is used self-serve by teams the Center of Excellence never directly staffed.Your quality bar becomes the internally recognized authoritative standard for AI specialist readiness.
