Senior Software AIML Engineer

Job description

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started. Join us to put AI to work for people.   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 Senior Software Engineer – AI Native Development, you will design, build, and operate next-generation AI-powered applications and platforms. You will combine strong full-stack software engineering fundamentals with hands-on expertise in modern AI/ML technologies to build production-grade experiences powered by LLMs, agents, retrieval, and intelligent automation. You will work across the technology stack—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML workflows, and cloud infrastructure. You will be expected to use AI-native development practices to accelerate engineering productivity while maintaining high standards for scalability, reliability, security, and quality. This role is ideal for an engineer who enjoys solving complex problems end-to-end and is excited about applying AI as a core engineering capability, not simply as an add-on to traditional software. What You'll Own • Design and build end-to-end full-stack applications, including frontend experiences, backend services, APIs, data layers, and cloud-native infrastructure. • Build production-grade AI/ML-powered capabilities using LLMs, RAG, embeddings, semantic search, agentic workflows, and intelligent decision-making. • Design and implement agentic architectures, including tool calling, orchestration, planning loops, memory, context management, and failure recovery. • Develop reliable AI-powered APIs and services that integrate frontier models and enterprise data securely and efficiently. • Build retrieval and grounding pipelines using vector search, hybrid search, semantic retrieval, re-ranking, and contextual enrichment. • Establish evaluation and observability mechanisms to measure AI quality, accuracy, latency, cost, reliability, and safety. • Take features from concept and prototype through production deployment and ongoing operation, with ownership of quality and reliability. • Work with product, design, platform, data, security, and other engineering teams to translate ambiguous problems into scalable technical solutions. • Contribute to architecture and design decisions, code reviews, engineering standards, and technical direction. • Mentor engineers and help raise the bar on full-stack engineering and production AI development practices. What You'll Do • Design and develop scalable, maintainable frontend applications and backend services using REST/GraphQL APIs, microservices, event-driven services, and distributed systems. • Work with modern frontend technologies such as React, TypeScript, JavaScript, or equivalent frameworks. • Build cloud-native applications with strong focus on scalability, performance, reliability, and security. • Own software delivery across development, testing, deployment, monitoring, and production operations. • Build applications leveraging LLMs, generative AI, embeddings, RAG, semantic search, and agentic workflows. • Integrate frontier AI models and SDKs such as OpenAI, Anthropic, Google, or equivalent platforms. • Apply prompt engineering, structured outputs, function/tool calling, context engineering, and model selection to real-world applications. • Design agent workflows that can reason, use tools, retrieve information, execute actions, and recover from failures. • Build AI evaluation frameworks and automated tests to measure model and application quality. • Balance model capability, accuracy, latency, scalability, and cost when selecting and integrating AI models. • Apply AI safety, security, privacy, governance, and guardrail practices to production AI systems. • Explore and adopt emerging AI technologies and rapidly turn promising capabilities into production-ready solutions. AI-Native Engineering Practices • Use AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, Windsurf, or equivalent tools as part of the software development lifecycle. • Apply AI to improve engineering productivity across coding, testing, debugging, documentation, code review, and system design. • Develop effective workflows for collaborating with coding agents while maintaining engineering quality and accountability. • Help establish best practices for AI-native software development across the engineering organization. • 5+