Staff Applications Dev Engineer

Staff Software Engineer – AI Native Development AI Security Incubation & Innovation Security and Risk Engineering About the team The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning. This is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like. The Role As a Staff Software Engineer – AI Native Development, you will be a hands-on technical leader responsible for the architecture, design, delivery, and evolution of major AI-powered software systems and subsystems. You will combine deep full-stack software engineering expertise with strong AI/ML-native development skills to solve complex, ambiguous problems and build production-grade systems at scale. You will own significant technical areas end-to-end—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML capabilities, and cloud infrastructure. Beyond your individual contributions, you will provide technical direction across a broader engineering area, make critical architecture and design decisions, establish engineering standards, and influence multiple engineers and teams. You will help shape how we build AI-native products and establish the technical foundation for the next generation of intelligent enterprise applications. This is a role for an engineer who can operate effectively at both architectural altitude and implementation depth—someone who can define the direction, make the difficult technical decisions, and still dive into the code when needed. What You'll Own A major product or technical subsystem end-to-end, including its architecture, design, implementation, scalability, reliability, security, and ongoing evolution. The technical vision and architecture for your area, including key design decisions and interfaces with other systems and teams. The quality and business/technical outcomes of your subsystem, with measurable targets for reliability, performance, AI quality, latency, cost, and customer impact. The architecture and engineering practices required to build AI-native applications at production scale. Technical direction for engineers working within your area, providing guidance through architecture, design reviews, code reviews, and hands-on technical leadership. The evolution of AI/ML capabilities such as agentic workflows, retrieval, model integration, evaluation, and intelligent automation within your product area. The technical strategy for balancing AI capability, engineering complexity, reliability, security, latency, and cost. What You'll Do Technical & Architectural Leadership Take highly ambiguous and complex problems and turn them into clear technical strategies, architectures, and executable plans. Own the architecture of major systems or subsystems and drive them from concept through production at scale. Make sound technical decisions under uncertainty and clearly articulate architectural trade-offs. Define system boundaries, interfaces, APIs, data flows, and integration patterns across multiple services and teams. Drive architecture and design reviews and establish a high engineering bar for scalability, reliability, security, maintainability, and performance. Identify architectural risks and technical debt and drive long-term improvements across your area. Influence technical direction beyond your immediate team through strong technical judgment and collaboration. Full-Stack Engineering Remain hands-on in building complex software across the stack, from frontend experiences and APIs to backend services, data systems, AI services, and cloud infrastructure. Design scalable full-stack architecture