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About This Role
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 About the role
We are hiring a Director of Product Management to lead the product vision and execution for a transformational agentic AI platform currently in MVP state. This role brings together cutting\-edge autonomous agent capabilities with enterprise governance, positioning you at the intersection of frontier AI and real\-world workflow impact.
You'll own the product direction for long\-running, self\-evolving autonomous agents designed for enterprise knowledge workers. The platform connects natively to ServiceNow's AI infrastructure, bringing governance, auditability, and workflow intelligence to every agent action\- enabling enterprises to deploy AI at scale with the controls they actually need.
This is a 0\-1 product leadership role on a team that operates like a startup inside enterprise. You'll be the builder who prototypes the next capability before it's on the roadmap, pulls the product forward by showing what's possible, and translates frontier AI research into platform primitives that drive consumption and adoption across the AI portfolio.
Why this role matters
Autonomous agents are moving from research to production. This role puts you at the center of that shift. You'll define how enterprises will harness agentic AI safely, govern it intelligently, and extract real value from it. Your product decisions will shape how the next generation of autonomous work gets done across large organizations.
The Impact You’ll Make
Operate as an AI\-native builder, not yesterday's PM
- Actively prototype, experiment, and ship using modern agentic development tools as a core part of your job.
- Engage directly with code: review implementations, stand up demos, and validate feasibility alongside engineering.
- Use hands\-on experimentation to de\-risk product bets and accelerate time\-to\-market.
- Stay at the leading edge of agentic AI, autonomous agents, open\-source frameworks, orchestration patterns, memory architectures, and translate that into concrete product capabilities.
Own the product surface area and platform primitives
- Define and drive the product's long\-term vision: autonomous agents enterprises trust to do real work, from desktop execution to orchestrated workflows across systems.
- Architect the roadmap for agentic platform capabilities: orchestration, memory and context management, action execution, endpoint interaction, and secure agent governance.
- Translate emerging agent patterns into scalable platform primitives that can be leveraged across this product and other ServiceNow AI offerings.
- Partner with frontier AI teams and the open\-source ecosystem to bring state\-of\-the\-art capabilities into enterprise production.
Own adoption and measurable outcomes at scale
- Define clear adoption paths and usage models for agentic capabilities across customer segments.
- Partner with go\-to\-market and outbound teams to ensure time\-to\-value and measurable impact.
- Establish metrics early. Task completion rates, success rates, latency, cost per action, and iterate rapidly against them.
- Engage with external ecosystem players and partners as needed to accelerate roadmap and adoption.
- Balance near\-term customer deliverables with long\-term platform differentiation and governance innovation.
Qualifications
Builder with hands\-on technical depth
- You don't just define products. You build, test, and iterate.
- 8\-10\+ years of product management experience, recent hands\-on experience shipping agentic products in the last 12–18 months. Years of PM tenure matter far less than what you've shipped recently and how deep you've gone technically.
- Comfortable working in codebases, standing up prototypes, and challenging engineering decisions with technical credibility.
- Background as a developer is a strong plus; exceptional PMs with adjacent technical depth are a match.
Agentic AI expertise and frontier AI fluency
- Strong understanding of modern AI systems and agent architectures: planning, memory, tool use, orchestration, multi\-step reasoning.
- Deep familiarity with the frontier of agentic AI: open\-source frameworks, agent loop patterns, LLM orchestration, and the fast\-moving research being published today.
- Ability to translate fast\-moving research into practical, productizable capabilities without waiting for consensus.
- You follow the agentic AI community, contribute to or build on open\-source projects, and stay connected to emerging patterns.
Enterprise product leadership at scale
- Proven product management experience (minimum 8 years), with hands\-on involvement in shipping complex products in ambiguous, fast\-moving environments.
- Track record of shipping 01 and scaling 1N products. You've brought something to market that didn't exist before and then scaled it.
- Ability to operate across multiple teams: engineering, design, GTM, partners and create alignment without authority.
- Thrives in ambiguity, creates structure where none exists, and pulls products forward through clarity and credibility, not process.
Systems thinker and platform builder
- You think in platforms, not features. You see how pieces connect and can architect primitives that multiply value across multiple products and use cases.
- You influence by showing, not telling. You paint a compelling picture of what's possible, one that VPs and executive teams want to be part of.
Nice to have
- Experience working on or shipping multi\-agent systems, agentic orchestration platforms, or agent deployment frameworks.
- Hands\-on involvement with LLM fine\-tuning, prompt engineering at scale, or domain\-specific model development.
- Track record of working with open\-source communities, contributing to or building on open frameworks.
- Prior experience in enterprise workflow automation, RPA, or low\-code/no\-code platforms. Understanding the operational constraints enterprises face.
- Familiarity with governance, compliance, and auditability requirements for enterprise AI systems.
- Experience working with frontier AI providers or integrating cutting\-edge models into production systems.
For positions in this location, we offer a base pay of $221,200 \- $387,100, 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.
From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.
Salary Context
This $221K-$387K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% 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 Required
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 $216,175 based on 270 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($304K) sits 41% above the category median. Disclosed range: $221K to $387K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ServiceNow AI Hiring
ServiceNow has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span San Diego, CA, US, Santa Clara, CA, US, San Francisco, CA, US. Compensation range: $241K - $445K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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