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Director of Engineering - AI Control Tower
ServiceNow is hiring for the job of Director of Engineering - AI Control Tower, Santa Clara, California (On-site). This is an AI Governance job in the governance, risk, and compliance field, with a posted range of $221,200 - $387,100. Review the full details below and apply directly with ServiceNow.
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. 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. 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 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. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.
Location and market context
This job is based in Santa Clara on-site. Local candidates benefit from being close to ServiceNow's teams and regional hiring market. Confirm the exact in-office expectation and any relocation support with the employer.
About AI governance jobs
AI governance sits at the intersection of policy, risk, and engineering. Teams are standing up model inventories, use-case intake and review, risk classification, and control monitoring as regulation and board scrutiny of AI intensify. Jobs like this one are typically evaluated against frameworks such as NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices.
How to position yourself for this AI governance job
Strong candidates emphasize experience translating policy into operational controls, working across legal, compliance, security, product, and data teams, documenting AI system risks, and supporting governance processes. In your resume and outreach, tie your experience to how ServiceNow would apply NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices, and lead with concrete outcomes rather than duties.
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