Staff Software Engineer

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.

As a Staff Engineer, you will independently lead the design, development, and delivery of complex, high-impact AI-native and cloud-native systems. You'll work at the intersection of UI, backend, and AI-driven engineering — architecting solutions that improve developer experience, expand platform capabilities, and drive engineering excellence across the team. This role requires the ability to balance technical depth with organizational impact, working across teams to solve ambiguous problems and drive platform improvements.

Responsibilities

Design and implement AI-Native product development, using cutting-edge technical approaches to deliver business value; Build high-quality, clean, scalable, and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit Testing, etc.); Drive complex, multi team technical initiatives and resolve architectural conflicts without management escalation; Own your code from design through implementation, test automation, and delivery; Collaborate with product managers to define feature sets that solve real customer problems; Lead architecture and code reviews, mentor senior engineers, and raise the front end bar across the organization; Build the evaluation infrastructure: golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection.; Bring judgment on where AI belongs in the engineering workflow, and set the guardrails for AI assisted development; Make informed architectural and technical trade-off decisions between latency, cost, scalability, reliability, and model quality; Stay current with emerging AI/ML frameworks, services, and techniques, evaluate and recommend adoption where applicable; Contribute to code and architecture standards, ensure adherence to established best practices

Requirements

Experience in leveraging critical thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry.; 8+ years of experience in software engineering, with a track record of delivering high-quality products.; 3+ years of hands-on work with LLM prompting, agent design, and retrieval systems (RAG, semantic search,; Experience integrating LLM APIs (Anthropic, OpenAI, Bedrock, Vertex, or similar), including tool/function calling, structured-output enforcement, and guardrails.; Experience building and debugging evaluation harnesses and metrics for AI features, such as accuracy, reg detection.; Working knowledge of embeddings, vector stores, and the failure modes of probabilistic systems, enough to debug and verify behavior in production.; Strong JavaScript/Node.js and Python, Java, with solid data modeling and query optimization; Strong knowledge of data structures, algorithms, object-oriented design, design patterns, and performance optimization; Front-end experience with modern frameworks (React, Lit, Angular, or Vue) for conversation-first experiences.; Automated testing (unit and integration), CI/CD, containers, and observability, plus on-call or incident experience with customer-facing systems.; Current, effective use of AI coding agents, with evidence of accountable delivery: precise specs, critica harnesses.; Demonstrated mentorship and a record of raising quality through code review.; Clear communication with both engineers and non-technical stakeholders.; Experience with LLM orchestration frameworks and vector databases (LangChain, LlamaIndex, pgvector, or similar).; Familiarity with automated testing frameworks (e.g., JUnit, Playwright, TestNG) and integrating tests into CI/CD pipelines; Understanding software quality principles including reliability, observability, and production readiness.; Ability to troubleshoot complex systems and optimize performance across the stack.; Experience with AI-powered tools or workflows, including validation of datasets, model predictions, and inference consistency.; Comfort with development tools such as IDEs, debuggers, profilers, source control, and Unix-based systems