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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 About the Role
As a AI Implementation Engineering Manager at Moveworks, you’ll be responsible for making customers successful with the Moveworks Platform by accelerating product adoption through various customer engagements and you are a critical, high\-leverage technical individual that operates at the intersection of Customer Success, Product Management, and Engineering.
Your primary mission is to translate complex customer business process challenges into innovative, high\-impact AI solutions built on our platform. This role provides full\-stack ownership of technical success, allowing you to not only deploy capabilities in the current product but also directly influence the product's evolution by ensuring that learnings are folded back into our core platform.
Core Responsibilities \& Impact
- Team Leadership: Ability to lead and develop teams, manage resources against objective\-based assignments, and translate functional plans into operational execution
- Full\-stack Ownership: Partner deeply with customers throughout the entire delivery lifecycle of AI Agents on the Moveworks Platform: Vision\-Lock, Solution Design/Architecture, Building, Tuning, and launch.
- Custom Solution Design: Architect, design and consult with customers to develop high\-impact technical AI solutions on the Moveworks platform by helping them leverage AI in novel and meaningful ways to solve complex business challenges.
- Integration and implementation: Integrate the Moveworks Platform to Customer enterprise system in an innovative, secure and performant manner.
- Product Partnership: Work closely with engineering and product teams on new product rollouts, and help drive product decision making by synthesizing on\-the\-ground customer feedback and technical gaps.
- Strategic Autonomy: Consult customers apply creative freedom in solution design to shape the customer’s Agentic AI roadmap.
About You
You are a technical generalist and a “do\-er” with a deep, intuitive understanding of complex systems and a relentless focus on customer impact. You thrive in a dynamic, high\-growth environment where you can both build and lead.
- Technical Acumen \& Curiosity Mindset: You are a rapid learner with high technical aptitude and strong generalist with instincts to quickly learn both new technical and business domains. You possess a curiosity to understand details from both a technical and a business perspective i.e: trying to understand: "what happens under the hood?" and “why is this done that way?”
- Technical Mastery: Strong grasp of API based systems integration, LLM\-based systems design including prompt engineering, context engineering, and data modeling.
- Product Excellence Obsession: You have product taste/judgement and are obsessed with building and delivering exceptional product experiences for users.
- Reusability: You share what works with the broader team, and help generalize solutions into reusable templates.
- Customer\-Centric Soft Skills: You have the communication skills required for deep customer partnerships, and you genuinely enjoy working directly with customers. You are compelled to develop and deliver compelling solution narratives through creation of high\-quality artifacts (e.g. architecture diagrams, solution proposals, product documentation, SOWs) as well as engaging presentations and demos, tailored to the technical and business awareness of the audience.
- Strategic Guidance \& Influence: Serve as a trusted advisor to customers, providing strategic direction to help them overcome technical and organizational obstacles. This includes developing and delivering context\-specific solutions, as well as upskilling customer teams to leverage the product independently.
- Entrepreneurial Drive / Grit: You aspire to a high\-growth career path, actively seeking to gain maximum exposure and learning across engineering, product, and business functions as quickly as possible. You lean into navigating through challenging business situations or antiquated legacy systems.
- Ecosystem Partnership: You possess a strong sense of personal accountability to both customers and internal teammates who rely on your expertise.
Qualifications To be successful in this role you'll have
- 6\-10\+ years of experience in a technical leadership role, such as a Forward Deployed Engineer, Solutions Engineer, Customer Success Engineer, Solutions Architect, Consulting Engineer or Software Engineer.
- Proven ability to lead and develop teams, manage resources against objective\-based assignments, and translate functional plans into operational execution
- Strong experience operating across multiple business functions or technical domains, demonstrating adaptability, quick\-to\-learn, and broad technical skills.
- Working expertise designing, building and launching full\-stack workflows, and automations, leveraging REST APIs, iPaaS automation (Workato, Azure Functions, AWS Lambdas, ServiceNow Flow Designer), or generic scripting (e.g., Python, JavaScript, Golang, etc.).
- You are willing to travel up to 25% of the time
Preferred Qualifications
- Familiarity with enterprise platforms (e.g. ServiceNow, Jira Service Desk, Zendesk, Workday, Okta, etc.) is a plus
- You have familiarity with Linux and Windows environments and using the command line.
- You have a great track record of driving successful technical adoption with medium to large\-sized enterprise projects
- You stay current with the latest AI tools and frameworks, and think about how to apply them thoughtfully to work smarter and build reliable, scalable solutions
For positions in this location, we offer a base pay of $137,700 \- $241,000, 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, 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 globaltalentss@servicenow.com 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.
Salary Context
This $137K-$241K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($189K) sits 13% above the category median. Disclosed range: $137K to $241K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
ServiceNow AI Hiring
ServiceNow has 16 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Mountain View, CA, US, Santa Clara, CA, US, Denver, CO, US. Compensation range: $129K - $264K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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