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Company Description About the Team:
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.
Job Description How We Work:
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.
What This Role Owns:
- You run two pods closely - hands-on through the full build lifecycle with two enterprise customers simultaneously, from agent shadowing through production go-live. You'll become one of the most experienced people in the company at knowing what it actually takes to ship a voice agent that works in the real world.
- Your second area of focus is 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. You're building the infrastructure that lets this team, and eventually our customers, move faster and build better agents.
Why This and Not Somewhere Else:
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.
Qualifications What You Bring:
- 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.
Even Better:
- 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.
Additional Information
For positions in this location, we offer a base pay of $143,200 - $243,400, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
Additional Information
Work Personas
We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.
Equal Opportunity Employer
ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.
Accommodations
We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance.
Export Control Regulations
For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.
Salary Context
This $143K-$243K range is below the median for AI Product Manager roles in our dataset (median: $193K across 99 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At ServiceNow, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($193K) sits 12% below the category median. Disclosed range: $143K to $243K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
ServiceNow AI Hiring
ServiceNow has 28 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Product Manager, AI Software Engineer. Positions span Santa Clara, CA, US, Mountain View, CA, US, Petah Tikva, Center District, Israel. Compensation range: $158K - $387K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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