Staff Software AIML 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 architectures using technologies such as React, TypeScript, Python, Java, Go, and modern cloud-native platforms.
• Build distributed services, event-driven systems, APIs, databases, caching, messaging, and scalable data pipelines.
• Ensure systems are observable, resilient, secure, and operationally excellent in production.
• Lead by example through high-quality implementation, testing, debugging, code reviews, and engineering practices.

AI/ML-Native Development

• Define and drive the adoption of AI-native architectures and engineering patterns across your technical area.
• Design and build production-grade LLM and agentic systems, including:
• Multi-agent orchestration
• Tool and function calling
• Planning and reasoning loops
• Context and memory management
• Retrieval and grounding
• Failure recovery and resilience
• Human-in-the-loop workflows
• Integrate frontier models from providers such as OpenAI, Anthropic, Google, or equivalent platforms, making informed decisions around model capability, cost, latency, and reliability.
• Design RAG and retrieval systems using embeddings, vector search, hybrid search, semantic retrieval, re-ranking, and enterprise data sources.
• Establish robust AI evaluation strategies and measurable quality metrics for AI-powered functionality.
• Drive AI observability covering model quality, latency, cost, failures, hallucination/error rates, and system behavior.
• Establish appropriate AI safety, security, governance, privacy, and guardrail mechanisms for production systems.
• Evaluate emerging AI capabilities and determine how and where they can create meaningful product or engineering value.

Technical Leadership & Influence

• Provide technical direction and mentorship to engineers across the workstream.
• Lead complex engineering initiatives through influence rather than organizational authority.
• Mentor senior and emerging engineers on architecture, system design, full-stack development, and production AI practices.
• Partner closely with product, design, platform, data, security, and other engineering organizations to translate customer problems into scalable technical solutions.
• Facilitate technical alignment across teams and resolve architectural disagreements through data, experimentation, and sound engineering judgment.
• Establish reusable patterns, frameworks, libraries, and engineering practices that improve productivity across teams.
• Help define the organization's approach to AI-native software development and AI-assisted engineering.

AI-Assisted Development

• Champion effective use of AI coding agents and development tools such as Claude Code, Codex, Cursor, Windsurf, or equivalent technologies.
• Establish engineering practices for using AI to accelerate development while maintaining code quality, security, testing, and accountability.
• Identify opportunities to use AI across the software development lifecycle, including design, implementation, testing, debugging, documentation, and code review.
• Share learnings and establish best practices that enable teams to become more effective AI-native engineering organizations.

What You Bring

• A strong track record of owning significant software systems or subsystems end-to-end in production.
• Deep full-stack engineering expertise with the ability to work across frontend, backend, APIs, data, AI services, and cloud infrastructure.
• Strong understanding of distributed systems, system architecture, data structures, algorithms, APIs, databases, scalability, reliability, and cloud-native development.
• Expert-level programming experience in Python, Java, Go, TypeScript, or equivalent languages.
• Hands-on experience designing and delivering AI/ML-powered production systems.
• Strong practical knowledge of modern AI technologies including LLMs, RAG, embeddings, vector search, agentic workflows, tool calling, model evaluation, and AI observability.
• Experience taking ambiguous problems from