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ServiceNow's Security and Risk Engineering organization is building next-generation AI-powered security solutions. This is a zero-to-one incubation focused on exposure analysis that ranks security work by exploitability rather than raw severity.
As a Senior Software Engineer – AI Native Development, you will design, build, and operate full-stack AI-powered applications combining strong software engineering fundamentals with hands-on expertise in modern AI/ML technologies. You'll work across the entire technology stack—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML workflows, and cloud infrastructure.
Key responsibilities include:
- Design and build end-to-end full-stack applications (frontend, backend services, APIs, data layers, cloud-native infrastructure)
- Build production-grade AI/ML 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 through prototype to production deployment and ongoing operation
- Work with product, design, platform, data, security, and engineering teams to translate ambiguous problems into scalable solutions
- Contribute to architecture, design decisions, code reviews, and technical direction
- Mentor engineers and raise the bar on full-stack engineering and production AI development practices
You'll apply AI-native engineering practices, using AI-assisted development tools and coding agents (Claude Code, Codex, Cursor, Windsurf) as part of the development lifecycle. You'll help establish best practices for AI-native software development across the organization.
Requirements:
- 5+ years of software engineering experience building and operating production-quality software
- Strong understanding of software engineering fundamentals, data structures, algorithms, design patterns, APIs, and distributed systems
- Hands-on experience developing full-stack applications with strength in both frontend and backend engineering
- Strong programming experience in Python, Java, Go, TypeScript, JavaScript, or similar languages
- Experience with modern frontend development, preferably React and TypeScript or equivalent
- Experience designing and building cloud-native applications, scalable APIs, microservices, databases, and distributed systems
- Hands-on experience building or integrating AI/ML-powered applications in production or near-production environments
- Practical understanding of modern AI concepts including LLMs, embeddings, RAG, vector databases/search, semantic search, agents, tool calling, and model evaluation
- Experience integrating frontier AI model platforms/SDKs such as OpenAI, Anthropic, or Google
- Ability to take AI/ML prototypes and turn them into reliable, scalable, maintainable production solutions
- Strong debugging, problem-solving, and system-design skills
- Strong communication and collaboration skills across engineering, product, design, data, and platform teams
- Demonstrated ownership of technical decisions and track record of improving code quality and engineering practices
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline, or equivalent practical experience
Nice to have:
- Experience designing multi-agent systems and agent orchestration frameworks
- Experience with AI evaluation, observability, guardrails, and responsible AI practices
- Experience with vector databases, hybrid retrieval, re-ranking, knowledge graphs, or enterprise search
- Experience with ML pipelines, MLOps, model monitoring, or inference optimization
- Experience with cloud platforms (AWS, Azure, GCP)
- Experience with Kubernetes, containers, CI/CD, and infrastructure-as-code
- Experience with AI coding agents
- Open-source contributions to AI/ML or developer tooling projects
- Cybersecurity, identity, risk, enterprise SaaS, or complex domain platform experience
