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ServiceNow's Security and Risk Engineering organization is building a new class of AI-powered exposure analysis that ranks security work by exploitability rather than raw severity. This is a zero-to-one incubation where the architecture is still evolving.
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. 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 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.
Key responsibilities include:
- Owning a major product or technical subsystem end-to-end, including architecture, design, implementation, scalability, reliability, security, and ongoing evolution
- Defining the technical vision and architecture for your area, including key design decisions and interfaces with other systems
- Taking highly ambiguous and complex problems and turning them into clear technical strategies, architectures, and executable plans
- Remaining hands-on in building complex software across the stack: frontend (React, TypeScript), backend services (Python, Java, Go), data systems, AI services, and cloud infrastructure
- Designing and building 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 human-in-the-loop workflows
- Integrating frontier models from providers such as OpenAI, Anthropic, and Google, making informed decisions around model capability, cost, latency, and reliability
- Designing RAG and retrieval systems using embeddings, vector search, hybrid search, semantic retrieval, re-ranking, and enterprise data sources
- Establishing robust AI evaluation strategies and measurable quality metrics for AI-powered functionality
- Driving AI observability covering model quality, latency, cost, failures, hallucination/error rates, and system behavior
- Establishing appropriate AI safety, security, governance, privacy, and guardrail mechanisms for production systems
- Providing technical direction and mentorship to engineers across the workstream
- Championing effective use of AI coding agents and development tools such as Claude Code, Codex, Cursor, and Windsurf
- Establishing engineering practices for using AI to accelerate development while maintaining code quality, security, testing, and accountability
Requirements:
- 8+ years of software engineering experience, or equivalent practical experience
- 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 with cloud-native technologies, distributed systems, APIs, databases, and scalable architectures
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience
- Cybersecurity or security-product experience is a plus
