Staff Software Engineer

About the team

The CRM & Industry Workflows (CRM&I) engineering organisation builds the products at the heart of ServiceNow's CRM vision: Workplace Service Delivery (WSD), Customer Service Management, Field Service Management, Sales & Order Management, and the shared CRM Foundation that connects them. Our teams span IDC, AMS, and EMEA, and our work ranges from core platform integrations to the agentic experiences that define how enterprises serve their employees and customers at scale.

Engineering here has fundamentally changed. AI is not a productivity add-on — it is the default operating model. We are rebuilding how products are specified, how code is generated and verified, and what individual engineering excellence looks like. If you want to be at the leading edge of that shift rather than watching it from the sidelines, this is the role.

The role

This Staff Software Engineer is a senior individual contributor embedded in the Workplace engineering team within CRM&I. You will own the architecture and technical design of Workplace Service Delivery capabilities — employee & customer self-service experiences, request and fulfilment workflows, and the WSD×CSM/FSM integration layer that makes those workflows real across the enterprise.

What distinguishes this role from a traditional Staff Engineer position is the AI-native mandate. You are not expected to type every line of production code — you are expected to decompose complex problems into precise specifications, direct AI coding agents across multiple workstreams in parallel, and own the verification harnesses that make agent-generated output trustworthy at scale. Value is measured by the quality of your judgment: architectural soundness, specification precision, and the rigour with which you verify that output matches intent — not by raw implementation speed.

You will work closely with engineering managers, product managers, UX designers, and platform architects to drive reference architectures for Workplace and mentor the next generation of technical leaders in the team.

What you get to do Translate problems into precise, agent-ready specifications

• Convert product requirements, business goals, and ambiguous problem statements into structured, testable specifications — with clear scope, interfaces, constraints, non-goals, and acceptance criteria — that both engineers and AI agents can act on with high accuracy.
• Drive technical design and architecture for Workplace Service Delivery, ensuring scalability, performance, and reliability across multi-tenant enterprise deployments.
• Define the data models, composable building blocks, and integration contracts that underpin employee self-service, request management, and WSD×CSM/FSM workflows.

Orchestrate AI agents to build and verify software

• Decompose work into agent-sized tasks and direct AI coding agents to generate, modify, and refactor production code across multiple files and services — supervising several workstreams in parallel.
• Author and maintain the context that drives correct results: system prompts, agent configuration files, architectural decision records, glossaries, golden examples, and agent-readable documentation.
• Determine when to delegate to an agent versus implement directly, making sound sequencing decisions before work begins and course-correcting quickly when output diverges from intent.

Own the verification and guardrail harness

• Design and maintain the constraints that make agent output trustworthy at scale: automated tests (including property-based and mutation tests), evaluation suites, CI/CD quality gates, and least-privilege execution environments.
• Rigorously evaluate code — human- or agent-generated — for correctness, spec adherence, security, performance, and maintainability, confirming behaviour matches intent and that edge cases and failure modes are genuinely handled.
• Build measurable evaluation frameworks — benchmarks, golden datasets, continuous evaluation pipelines, and drift detection — that hold production AI to the same rigour as deterministically testable software.

Own quality, security, and AI reliability in production

• Take end-to-end responsibility for the software you ship: integrate changes safely, monitor production, and feed production signals back into specs and evaluation sets.
• Defend against risks specific to AI-integrated systems: prompt injection, sensitive-data and secret leakage, tool-access governance, and model abuse — translated into concrete engineering controls.
• Monitor production AI for hallucinations, behavioural drift, and safety regressions; maintain agent observability through reasoning traces and model quality telemetry; implement rollback mechanisms when autonomous behaviour deviates from intent.

Elevate engineering craft across the team

• Treat the development process itself as a product: trace failures back to missing specs or context gaps, refine prompts, tests, and guardrails, and raise the team's overall throughput and reliability — not just your own.
• Mentor and grow lead engineers and senior engineers, investing in their AI-native fluency, architectural judgment, and verification rigour.
• Collaborate with product managers and UX designers to translate requirements and mockups into fully functional, accessible, and delightful employee experiences.

To be successful in this role Experience

• 10+ years of experience designing and building scalable, reusable enterprise software products and components.
• Demonstrated track record of delivering real production systems using AI-native methods — agentic coding tools, AI-assisted workflows, and autonomous agent architectures — not demos or experiments.
• Hands-on experience in CRM, ITSM, workplace service delivery, or related enterprise workflow domains preferred.

AI-native engineering fluency

• Agentic AI and autonomous systems. Practical, current proficiency with agentic coding tools and agent architectures — planning loops, dynamic tool invocation, memory management, execution policies, and multi-agent collaboration — that select and sequence actions reliably in production.
• Spec precision. The ability to define problems with enough rigour and explicit detail that an AI agent can implement them correctly, making mental models and acceptance criteria fully explicit rather than leaving them implicit.
• Context engineering. Skill in designing what information models receive, when it is retrieved, and how context evolves. Includes assembling instruction files, documentation, examples, and architectural constraints, as well as architecting retrieval systems, vector databases, and long-term memory frameworks.
• Verification and harness design. Experience building the guardrails that make agent output reliable: automated tests (including property-based and mutation testing), evaluation suites, CI/CD quality gates, and provenance and permission controls.
• Outcome evaluation. Ability to develop measurable frameworks — benchmarks, golden datasets, continuous evaluation pipelines, and drift detection — that hold production AI to the same rigour as deterministically testable software.
• Security and reliability mindset. Working knowledge of risks specific to AI-integrated systems: prompt injection, sensitive-data leakage, tool-access governance, and model abuse, translated into concrete engineering controls and least-privilege practices.

Technical depth

• Strong command of data structures, algorithms, system design, testing, and modern software development practices.
• Solid data modelling background — relational and otherwise — with experience designing enterprise-grade data models where correctness and extensibility are first-class concerns.
• Solid AI/ML fundamentals: model training and evaluation, embeddings, and the probabilistic failure modes of LLMs, sufficient to reason about, debug, and verify model-driven behaviour in production.
• Architectural judgment: proven ability to make sound design and sequencing decisions under uncertainty and to judge when to delegate to an agent versus implement directly.

Collaboration and communication

• Strong written and verbal communication — able to operate credibly with product, UX, platform, and engineering stakeholders across time zones.
• Comfortable managing multiple priorities simultaneously with the ability to make clear decisions without waiting for perfect information.
• Conviction to influence the product vision and roadmap with a strong point of view, paired with the intellectual honesty to update that view when the evidence changes.

Even better if you have

• Experience with the ServiceNow platform — scoped application architecture, Glide APIs, Service Catalog, or workflow engine internals.
• Hands-on exposure to multi-tenant SaaS architecture and the operational realities of shipping to thousands of enterprise customers simultaneously.
• Deep knowledge of employee self-service, HR service delivery, or workplace operations domain models.
• Experience building and operating agentic systems in a regulated or enterprise-compliance context.
• Familiarity with integration patterns for connecting workplace, CRM, and field service systems — REST, eventing, data federation.

Why ServiceNow

We provide competitive compensation, comprehensive benefits, and a professional environment built on collaboration and inclusion. This is an organisation where individuals with strong aptitude and conviction grow fast — working on some of the most advanced enterprise technology in the world, with teams that take the craft seriously.

If you want to be at the frontier of what AI-native software engineering actually looks like in production — not in a lab, but at enterprise scale — this is where that work is happening.

• Determines methods and procedures on new assignments and may coordinate activities of other personnel
• Exercises judgment in selecting methods, techniques and evaluation criteria for obtaining results.
• Networks with key contacts outside own area of expertise.
• Applies technical knowledge to determine solutions and solves complex problems across departments.
• Leads projects that may cross departments. Has to lead other team members.
• Typically requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience; or equivalent experience.

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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