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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 The Role
The AI Control Tower is ServiceNow's central hub for governing every AI model, agent, and dataset across the enterprise. We're building the infrastructure that enterprises will depend on as agentic AI moves from experiment to production — and we need a Director of Engineering who has been in that position before: shipping a 1\.0 product, fighting for its first users, and figuring out how to turn early traction into durable adoption.
This isn't a role for someone who has managed mature systems. It's for someone who has built something from scratch — in a small, fast company where there was no playbook — and who is now ready to do it at the scale and ambition of ServiceNow. Reporting to the VP of Engineering, AICT, you will lead one or more product areas within the AICT platform, with scope and ownership evolving as the product and team grow.
You are AI\-native. You don't just understand AI conceptually — you use it daily, you build with it, and you bring that instinct into how your team designs and ships.
What You Will Do
Product Delivery \& Roadmap Execution Own the engineering roadmap for your AICT product areas — translating product requirements into technical plans, managing delivery across a focused engineering team, and maintaining the velocity needed to meet the demands of a fast\-moving market. You have done this before and know the difference between motion and progress.
1\.0 to Adoption Drive the full arc from early release to meaningful customer adoption. You understand that shipping a 1\.0 is only half the job — the other half is learning fast, iterating based on what real customers do (not what they say), and building the feedback loops that compound adoption over time.
AI\-Native Engineering Set the standard for how your team uses AI in its own development practice — from AI\-assisted code review and testing to designing platform features that assume AI agents are first\-class users. You lead by example: your team should see you using AI tools as fluently as you use a terminal.
Team Building Recruit, develop, and retain a high\-performing engineering team. You are a strong technical voice who can also grow engineers — you give clear feedback, create stretch opportunities, and build the kind of team culture that makes people want to stay and do their best work.
Cross\-Functional Execution Work closely with Product Management, Design, and partner engineering teams to align dependencies and ship coherent experiences. Represent engineering in planning and review forums with senior leadership.
Qualifications Required
- 10\+ years in software engineering with 4\+ years leading engineering teams (as a manager of managers or strong senior manager)
- Demonstrable startup experience at a company with fewer than 100 employees, where you were hands\-on building and shipping a product — not just working at a startup, but building one
- Proven experience driving adoption of a 1\.0 or early\-stage product: you have the scars from a launch that didn't go perfectly and the wins from figuring out how to turn it around
- Deep technical foundation in cloud\-native SaaS, including multi\-tenant architectures, large\-scale API design (REST/gRPC), Kubernetes orchestration, multi\-cloud platforms (AWS, Azure, GCP), Infrastructure as Code (Terraform, CloudFormation, Helm)
- Strong understanding of database schema design, data modeling, and modern data platforms, including relational, graph, vector, columnar, OLAP, and streaming data architectures, with the ability to design scalable data models for AI and enterprise workloads.
- database schema design, , data modeling, and event\-driven architectures.
- AI\-Native Engineering: Lead the adoption of AI across the entire engineering lifecycle—from product strategy, PRDs, UX, architecture, implementation, code reviews, debugging, testing, security, CI/CD, deployment, observability, APM, operations, and documentation—to improve engineering productivity, software quality, reliability, customer experience, and delivery velocity.
- Ability to operate at both the architectural level and the pull request level when it matters
- Track record of building and retaining engineering teams, including in competitive talent markets
Preferred
- Experience building governance, observability, or compliance products — you understand why enterprises care about auditability and have designed for it
- Familiarity with agentic frameworks and the operational realities of running agents in production (tool reliability, context management, failure modes)
- Background in enterprise security, IAM, or data governance
- Familiarity with MCP, OpenAPI, or other AI interoperability standards
- MS or PhD in Computer Science, AI, or a related technical field
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/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($304K) sits 42% above the category median. Disclosed range: $221K to $387K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ServiceNow AI Hiring
ServiceNow has 11 open AI roles right now. They're hiring across AI Engineering Manager, AI/ML Engineer, AI Agent Developer, AI Product Manager. Positions span Santa Clara, CA, US, San Francisco, CA, US, Kirkland, WA, US. Compensation range: $243K - $445K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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