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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 Distinguished Product Manager to lead the product vision and execution for a transformational frontier Ai agent 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.
- 12+ 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 $225,200 - $394,100, 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 $225K-$394K range is above the 75th percentile for AI Agent Developer roles in our dataset (median: $175K across 28 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At ServiceNow, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $215,000 based on 203 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($309K) sits 44% above the category median. Disclosed range: $225K to $394K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
ServiceNow AI Hiring
ServiceNow has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span Dallas, TX, US, San Diego, CA, US, New York, NY, US. Compensation range: $158K - $394K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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