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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 seeking a highly strategic and technically fluent AI for Operations Director to lead our AI for Operations portfolio within the AI Acceleration organization.
This leader will be responsible for defining and executing the AI strategy supporting Global Operations \& Planning (GO\&P), Revenue Operations, Sales Operations, Planning, Strategy, Incentive Compensation, Workforce Planning, and Business Operations teams. The role combines people leadership, AI solution delivery, stakeholder management, and technical oversight to accelerate the adoption of AI across operational workflows.
You will lead a team of AI Engineers while partnering closely with Data Engineers, Program Managers, Digital Technology, Business Operations leaders, and executive stakeholders to identify opportunities, prioritize investments, and build scalable AI\-powered solutions that improve how we operate globally.
The ideal candidate understands both business operations and modern AI technologies. They can translate complex operational challenges into scalable AI solutions while building high\-performing engineering teams capable of delivering measurable business outcomes.
Your work will directly impact productivity, decision quality, planning accuracy, operational efficiency, and employee experience across some of ServiceNow's most critical business functions. By aligning AI capabilities with business priorities, you will help accelerate ServiceNow’s journey toward becoming a $30B\+ business.
Key Responsibilities:
- Lead and develop a team of AI Engineers responsible for designing, developing, deploying, and maintaining AI\-powered solutions for Operations teams.
- Own the AI for Operations portfolio, including roadmap planning, prioritization, resource allocation, stakeholder alignment, and business value realization.
- Partner with GO\&P, Revenue Operations, Sales Operations, Planning, Strategy, Workforce Planning, and Business Operations leaders to identify high\-impact AI opportunities.
- Drive the design and deployment of agentic AI solutions, copilots, workflow automation, knowledge systems, predictive analytics, and operational intelligence capabilities.
- Establish technical standards, engineering best practices, governance frameworks, and responsible AI guidelines across the Operations AI portfolio.
- Collaborate closely with Data Engineers to ensure scalable, secure, and compliant data foundations for AI solutions.
- Manage project intake, prioritization, portfolio health, and capacity planning across AI Engineering resources.
- Define and track success metrics tied to business outcomes. Ie: Productivity gains, cycle\-time reduction, forecast accuracy, process automation, and user adoption.
- Partner with Adoption Leads and Enablement teams to ensure successful rollout and sustained value realization of AI solutions.
- Evaluate emerging technologies, vendors, and platforms to continuously improve our AI ecosystem and delivery capabilities.
- Communicate progress, risks, investment recommendations, and business impact to senior leadership and executive stakeholders.
A Day in the Life:
- Your day might begin with a portfolio review of active AI initiatives, assessing delivery status, resource allocation, and business impact across multiple Operations functions.
- Mid\-morning, you facilitate a prioritization session with Revenue Operations and Planning leaders to evaluate new AI opportunities and align on the highest\-value investments for the upcoming quarter. Later, you review solution architecture proposals from AI Engineers, providing guidance on scalability, governance, and integration with enterprise platforms.
- In the afternoon, you meet with executive stakeholders to present portfolio performance, adoption metrics, business outcomes, and strategic recommendations for future investments.
- You close the day coaching members of your AI Engineering team, removing roadblocks, and ensuring delivery teams remain focused on the initiatives that create the greatest impact for the business.
Ideal Candidate Profile:
- Proven leader who can build, develop, and inspire high\-performing technical teams.
- Deep understanding of operational business processes and how AI can transform them.
- Experienced in translating executive priorities into actionable technical roadmaps.
- Comfortable operating at both strategic and technical levels.
- Strong systems thinker capable of identifying scalable opportunities across complex organizations.
- Exceptional collaborator who can influence across functions, levels, and geographies.
- Curious, autonomous, data\-driven, and outcome\-oriented.
- Passionate about responsible AI and measurable business impact.
Qualifications Experience:
- 8\+ years of experience in AI, data, analytics, automation, solutions engineering, operations, consulting, or related fields.
- 3\+ years of people leadership experience managing technical or cross\-functional teams.
- Proven track record leading AI, automation, analytics, or digital transformation initiatives at enterprise scale.
- Experience managing portfolios, roadmaps, and investment prioritization across multiple stakeholders.
- Demonstrated success building and deploying AI\-powered solutions that drove measurable business outcomes.
- Experience working within Sales Operations, Revenue Operations, Planning, Strategy, Finance, Customer Success, or related operational functions.
- Experience operating in highly matrixed global organizations.
Technical Expertise:
- Strong understanding of LLMs, agentic AI architectures, workflow automation, and enterprise AI platforms.
- Experience with Copilot Studio, Snowflake AI, Databricks, APIs, integrations, and modern cloud architectures.
- Familiarity with data engineering, governance, security, and compliance requirements.
- Experience evaluating and scaling both internally developed and third\-party AI solutions.
- Proven experience leveraging AI technologies, including Claude, Copilot, and agentic workflows to improve productivity and business performance.
Leadership \& Communication:
- Exceptional executive communication and storytelling skills.
- Strong program management and prioritization capabilities.
- Ability to influence without authority across complex organizations.
- Skilled at navigating ambiguity and driving alignment among senior stakeholders.
- Proficiency in English is required. Additional languages (e.g., Spanish, French, German) are highly valued.
What Success Looks Like:
Within your first 12 months, you will have:
- Successfully deployed and measured return on AI4Ops 2026 roadmap.
- Built and operationalized an AI\-first operating model across Sales Operations.
- Increased automation maturity across critical value streams.
- Improved Sales Operations productivity through AI\-enabled workflows and intelligent automation.
- Increased AI Engineers’ knowledge and autonomy in ServiceNow and 3rd\-party AI technology.
- Helped design a sustainable AI adoption culture supported by governance, training, and continuous improvement.
For positions in this location, we offer a base pay of $173,200 \- $303,100, 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 $173K-$303K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ServiceNow, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($238K) sits 9% above the category median. Disclosed range: $173K to $303K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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