Job detail for Staff Software Engineer

Company 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.

Job Description

The Staff Software Engineer (IC4) on AI-Native ITSM designs, builds, ships, and operates capabilities whose core behavior is model-driven rather than explicitly authored—agentic and conversational experiences that interpret an IT operator's or end-user's intent, reason over incident, request, change, and knowledge contexts, invoke tools and workflows, and act on the user's behalf across the incident-to-resolution and request-to-fulfillment lifecycles. An agent operating on incidents and requests touches SLA compliance, incident classification accuracy, knowledge fidelity, and user trust—a confidently wrong diagnosis is not a suggestion, it is a failed resolution. At IC4 the engineer owns AI design decisions across the domain, not within a single feature, and owns the correctness of what ships whether a person or an agent produced it. #### What You Do - Build AI-native capability across the IT service management: Design and ship features built around agentic behavior—intent interpretation, multi-step reasoning, tool invocation, and action on the user's behalf—together with the data models, integrations, and channels that make them usable in production. In AI-Native ITSM this spans guided request categorization and routing, natural-language incident description and classification, conversational incident status and in-flight updates, intelligent assignment and routing, knowledge article recommendation and summarization, and auto-remediation orchestration. - Design AI-driven autonomous workflows: Decompose ITSM processes into the steps and decision points an agent can execute—determining where autonomy is appropriate, where a checkpoint with a person is required, and how exceptions, retries, and hand-back are handled. The design must make that distinction structural rather than advisory. - Author and maintain agentic instructions as engineering artifacts: Write, structure, and version the system instructions, role definitions, tool descriptions, guardrails, and escalation paths that govern agent behavior in the domain—under code review, source control, and regression coverage. Own the shared instruction and tool-description surface that adjacent teams build against. - Build automated evaluation and test non-deterministic behavior: Design and operate the evaluation that makes change safe—golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, and drift detection—plus adversarial, jailbreak, grounding, and tool-selection testing. - Design conversational experiences across channels: Build experiences that hold context across turns, hand off cleanly between automated and live agents, and behave consistently across chat and voice—accounting for what voice imposes: latency budgets, barge-in, speech recognition error, disambiguation, and explicit confirmation before consequential actions. - Specify precisely and direct AI coding agents: Convert requirements into testable specifications with explicit scope, constraints, non-goals, and acceptance criteria; decompose work into agent-sized tasks; supervise several workstreams in parallel; and review agent output for correctness, spec adherence, security, and maintainability. You own the result regardless of what produced it. - Own quality, safety, and reliability in production: Monitor conversation quality, containment, hallucination rate, tool-selection error, and unsafe or unauthorized action. Defend against prompt injection and data leakage across integration surfaces. Maintain reasoning-trace observability and model rollback mechanisms, and feed production failures back into specifications and evaluation sets. Lead root-cause analysis when agentic behavior deviates from intent. - Ground it in solid full-stack delivery: Build the application, APIs, data models, and integrations around these capabilities—front-end experiences for IT operators and end-users, server-side logic, and the connections to knowledge, asset, change, and fulfillment systems—with the CI/CD, observability, and upgrade-safe extensibility expected of production software. - Collaborate across product, design, and engineering: Partner with product managers, designers, conversation designers, and engineers to define success criteria and communicate capability and risk clearly. Mentor IC1 to IC3 engineers, and raise the team's practices around instruction authoring, evaluation, and accountable agent use.

Qualifications

Required Experience and Skills - Production track record: A demonstrated record of building, shipping, and operating production software, including hands-on delivery of AI-native application features that real users depend on. Depth and demonstrated judgment matter more than tenure; typically around 9+ years of relevant software engineering experience. - Agentic delivery experience: Direct experience authoring agentic instructions and prompts, designing AI-driven autonomous workflows, and building the evaluation and testing that verifies them. This is required, not preferred. - Production AI integration: Experience integrating large language model APIs and retrieval-grounded features, including agent orchestration, tool and function calling, and structured output enforcement. - Domain ownership: Demonstrated ownership of a complex domain or subsystem end-to-end, including the architectural decisions, migrations, and operational consequences. - Engineering fundamentals: Strong command of data structures, algorithms, system design, APIs, data modeling, and testing. Proficiency in front-end development with a modern component framework, server-side development, relational data modeling, and REST and GraphQL API design. - Applied machine learning literacy: A working command of the concepts that govern how these systems behave—evaluation, embeddings, and the probabilistic output and failure modes of modern models—sufficient to reason about, debug, and verify model-driven behavior in production. - Accountable use of AI coding agents: Current, effective use of AI coding assistants and agents with evidence of accountable delivery: precise specification, critical review of generated output, and verification harnesses. - Operational experience: Hands-on CI/CD, containerized workloads, and observability experience, plus direct on-call and incident-command experience with customer-facing systems. - Mentorship: Demonstrated mentorship of less-experienced engineers and a record of raising quality through code review. - Education: Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Preferred Experience - ITSM domain depth: Incident management, request fulfillment, change management, problem management, knowledge management, or SLA-driven operations at a depth sufficient to challenge a requirement rather than only implement it. - Evaluation and observability tooling: Evaluation frameworks, prompt and instruction management tooling, tracing for model-driven applications, and analysis of production transcripts at scale. - Conversation design partnership: Working alongside conversation or content designers on dialogue flow, tone, and error-recovery design. - Forward-deployed delivery: Building against a customer's data, integrations, and ITSM tooling, and tuning instructions and evaluation sets in their environment. - Platform depth: ServiceNow ITSM experience including scoped applications, Flow Designer, UI Builder, Automated Test Framework, and upgrade-safe extension patterns.

Additional Information

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity,  veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.

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