Director of Engineering, ServiceNow

• 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
• 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
• 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
• 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

Desirable

• 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
• 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
• 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
• 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