Principal Inbound Product Manager

Company Description

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

Job Description

What you get to do in this role:

Enterprise AI is ServiceNow's horizontal capability team, building the operating model, technical foundation, and Customer Zero motion for AI at enterprise scale — including AI Control Tower, the AI agent orchestrator, and the AI agent fabric on the ServiceNow AI platform.

As Principal Product Manager, Intelligence Systems, you will serve as the senior product leader for Enterprise AI's intelligence systems portfolio, defining the vision, shaping the roadmap, and driving execution for a workforce of AI agents built on models, workflows, and intelligence services.

In this role, you will:

- Define and communicate the product vision for Enterprise AI's intelligence systems portfolio, including AI/ML models, AI Control Tower, foundation services, evaluation and observability, and value instrumentation.

- Own the roadmap from opportunity framing through capability, adoption, measurement, and iteration, making hard prioritization calls on what to fund, sequence, pause, or stop.

- Drive end-to-end product execution across AI engineering, data and ML, infrastructure, security, GRC, and domain teams, turning ambiguous AI opportunities into clear requirements, milestones, and launch plans.

- Build structured Customer Zero feedback loops that turn ServiceNow's own internal AI adoption into credible product learning, not just status reporting.

- Bring technical depth to product and architecture decisions involving GenAI, agentic systems, RAG, LLM gateways, AI runtimes, evaluation frameworks, and AI security.