Senior Inbound Product Manager, CRM Voice AI

ServiceNow•Santa Clara, CA

2d•$143,200 - $243,400•Hybrid

About The Position

CRM Voice AI builds autonomous voice agents for customer service that resolve customer interactions end-to-end and drive significant value for the business. We're a team inside ServiceNow's CRM business unit, shipping production voice AI with enterprise customers today and scaling aggressively. Our agents run on a real-time streaming pipeline with best-in-class ASR, TTS, and LLM orchestration, integrated into the same platform where many of our customers already manage their front, middle, and back-office workflows. For those that don't, the voice agent is often the tip of the spear that brings them in. We compete directly against well-funded AI startups and CCaaS incumbents, and we're winning. We work in pods - small, dedicated teams embedded with a single enterprise customer, building their voice agents from first call to self-sufficiency. A pod owns the full lifecycle: agent shadowing, conversation design, build, production go-live, and the handoff that makes the customer self-sufficient so the pod can move on to the next one. Each pod pairs forward-deployed engineers with a product manager who owns both the customer outcome and the product learning that comes from it. Every PM on this team runs at least one pod directly and has visibility across others. You're in the customer's world - on their calls, in their systems, solving their problems - while simultaneously shaping what the product becomes. The learnings from your pods feed directly into the platform roadmap and into repeatable, productized assets that scale beyond any single customer. This role puts you directly in front of enterprise customers running production voice AI from day one. You're not waiting to earn customer exposure - it's the job. The pod model means you'll accumulate more production voice AI experience in a year than most PMs get in several, across different industries, different contact center environments, and different conversation types. At the same time, the evaluation frameworks and agentic design patterns you build have reach well beyond your own pods. This team has a direct seat at the platform table, so the tooling you create shapes how ServiceNow thinks about voice agent quality at scale.

Requirements

Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
5+ years of product management experience, or equivalent experience in a technical role such as engineering, solutions consulting, or technical program management where you owned product outcomes.
Comfort with data and evaluation. You think naturally about how to measure whether something is working and how to build systems that do it at scale.
Experience working directly with customers or end users on technical products. You don't need enterprise sales experience, but you need to have been in the room where the work happens.
Strong technical foundation. You can read code, understand API contracts, and hold your own with engineers on system design.
An instinct for repeatable process. You see a problem solved once and immediately think about how to solve it for every customer.

Nice To Haves

Experience with conversational AI, voice technology, or contact center operations.
Background building evaluation frameworks, QA automation, or analytics products.
Familiarity with agentic AI design patterns and how to give an AI agent reliable, structured behavior alongside open-ended reasoning.
Coding ability in any language. You won't write production code, but PMs on this team who can prototype, script, and dig into data move faster.
Experience with telephony environments: SIP, CCaaS platforms, real-time media handling.

Responsibilities

Run two pods closely - hands-on through the full build lifecycle with two enterprise customers simultaneously, from agent shadowing through production go-live.
Own the tooling and intelligence layer that makes voice agents better and easier to build. This spans automated evaluation frameworks (how do we systematically measure whether a voice agent is good?), analytics (what happened on that call and why?), admin experience (how does a customer manage and improve their own agents?), and smarter agentic design patterns like deterministic procedures that give agents reliable behavior on structured tasks.
Build the infrastructure that lets this team, and eventually our customers, move faster and build better agents.