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About This Role
Company Description
It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI\-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud\-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.
Job Description Role Description
As a Sr. AI Delivery Acceleration Specialist, you will design, refine, and govern the prompt workflows and content production patterns that transform how ServiceNow Expert Services deliver customer implementations. Working within the Delivery Acceleration team, you will turn what previously required weeks of manual effort into AI\-accelerated delivery that produces customer\-ready outputs in hours.
You will own the design and quality of AI\-powered tools that support ServiceNow Expert Services' go\-to\-market and implementation efforts. Critically, you must understand the professional services go\-to\-market motion — how services are scoped, sold, estimated, and delivered — because the solutions and processes you build must align with how consultants and sales teams position and execute customer engagements. Your work directly impacts delivery speed, quality, margin, and the customer experience.
You will operate in a fast\-paced two\-week sprint cadence with monthly releases, partnering with AI Architects, Solution Architects, Expert Services, and the broader Delivery Acceleration team across AMS, EMEA, and APAC\+J.
This is not a software engineering role. There is no coding, no platform development, and no model training. The work is designing prompts and workflows that make AI useful for delivery practitioners, quality\-checking the outputs before they reach anyone, and building the repeatable methodology that scales across a global delivery community. The right candidate is closer to a technical business analyst with deep AI instincts than a software engineer.
What You Get to Do in This Role
Design and Build Prompt Workflows
- Design multi\-step prompt chains within our AI delivery platform that autonomously generate ServiceNow implementation scope, estimates, customer\-facing presentation decks, demo companions, and other artifacts that speed delivery
- Author, refine, and version\-control prompt templates; manage the extract refine archive promote workflow for the 125\+ template library
- Encode best practices and ServiceNow scoping standards into reusable prompt patterns across product areas (ITSM, CSM, HRSD, SPM, FSO, and others)
- Partner with AI Architects on agent direction and with the platform administrator on library structure — Architects own platform and agent architecture; the Specialist owns prompts, content patterns, and quality
Lead Quality Assurance and Prompt Optimization
- Review and quality\-check all AI\-generated content before it reaches the Expert Services community; develop guardrails that ensure completeness, traceability, formatting consistency, and accuracy against source inputs
- Design systematic evaluation approaches that measure output consistency and adherence to delivery standards across varied scoping inputs
- Iterate on prompts based on user feedback, audit signals, and output analysis — treat prompts as production assets with version control and quality gates
- Support office hours across AMS, EMEA, and APAC\+J, plus delivery\-lead validation cycles
Integrate AI with Professional Services Go\-to\-Market and Delivery
- Understand the full professional services lifecycle and partner with delivery consultants, Solution Consultants (SCs), and Account Executives (AEs) to validate that AI\-generated outputs are usable in real customer contexts — feeding signals back into the improvement loop
- Collaborate with the Sales Acceleration team to ensure AI\-powered delivery capabilities are accurately represented in pre\-sales motions and customer\-facing demonstrations — supporting accurate estimation that reflects realistic Professional Services delivery economics
- Partner with the Now Engage platform team to embed AI capabilities into the customer and partner delivery experience
- Contribute to packaging AI\-accelerated delivery into repeatable service offerings that services and license sales teams can position and sell
Drive Measurement and Continuous Improvement
- Establish quality metrics for prompt outputs (accuracy, rework rates, consultant acceptance, time savings, consistency) and analyze performance signals to surface failure patterns and expansion opportunities
- Contribute to the delivery acceleration roadmap by identifying where AI can fill capability gaps or replace manual processes
- Document prompt patterns, content production best practices, and lessons learned to build organizational knowledge
Qualifications To be successful in this role you have:
- Hands\-on prompt engineering experience: designing prompts for consistent, structured output; iterating based on failure analysis; treating AI\-generated content as a first draft requiring quality assurance before use — not a finished product
- Pre\-sales or delivery consulting experience with direct ownership of scope summaries, ROM estimates, SOW drafts, or equivalent customer\-facing delivery artifacts
- Advanced written communication and presentation skills. A portfolio of customer\-facing or executive\-facing work is required — candidates without a portfolio will not advance.
- Detail\-oriented quality assurance mindset with experience reviewing AI\-generated or templated content for accuracy, consistency, and traceability against source material
- Strong understanding of professional services delivery operations — how engagements are scoped, sold, estimated, staffed, and delivered
- Self\-directed working style across AI tools, Excel, presentation software, and SharePoint; comfortable serving as a second pair of eyes on prompts before promotion to shared spaces
- Experience working in agile, sprint\-based environments with measurable delivery outcomes
Preferred Qualifications
- ServiceNow domain knowledge, ideally across multiple product areas, and direct experience producing or reviewing implementation artifacts such as user stories, scope summaries, or SOWs.
- ServiceNow platform certifications (CSA, CIS\-ITSM, CIS\-CSM, or similar)
- Experience supporting scoping and discovery calls with SCs and AEs
- Business Analyst or Project Manager background
- Comfort running office\-hours\-style enablement sessions across time zones
- Enablement content creation experience: recorded demos, training assets, or similar
- Background in management consulting or professional services delivery operations
This Role Is Not a Fit If
- You are primarily seeking a software engineering, machine learning, or platform development role. This role involves no coding, model training, or platform engineering.
- You use AI tools but do not review or quality\-check the output before sharing. A quality\-first mindset is non\-negotiable.
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, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, 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. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ServiceNow AI Hiring
ServiceNow has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span CA, US, San Francisco, CA, US, Mountain View, CA, US. Compensation range: $243K - $243K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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