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Salary: USD 199,100 - 348,400 / annual
ServiceNow is seeking a Principal Software Engineer to set technical direction for agentic AI capabilities in the HR Service Delivery (HRSD) domain. This is a high-impact staff-level role focused on architecting how AI agents interpret employee, manager, and HR agent intent, reason over organizational context (profiles, cases, catalogs, policies, knowledge), and act safely on behalf of users.
You will own the architecture, standards, and evaluation infrastructure that enable multiple teams to ship agentic and conversational experiences safely—not a single feature set. This is distinct from ML research (you do not train foundation models) and from traditional full-stack work. The role centers on engineering natural-language constructs (instructions, prompts, context, tool descriptions, guardrails) with the same rigor as code, and establishing correctness through evaluation at scale rather than fixed assertions.
Key ownership areas:
- AI architecture for the HR domain: agent decomposition and composition, reasoning placement, context assembly and bounding, tool exposure, autonomy delegation, and multi-release decisions around model selection, orchestration, build-versus-adopt tradeoffs, and cost/latency/quality optimization.
- Autonomy boundary: define which HR actions agents may take autonomously, which require human decision points, and which should never be attempted. Enforce structural constraints for actions affecting pay, employment status, restricted records, or employee relations.
- Shared instruction and tool description surface: treat as a versioned product contract with upgrade-safe extension points, deprecation paths, and compatibility guarantees for customer configuration and override.
- Evaluation infrastructure: own golden datasets, multi-turn test suites, judge calibration, CI gates, and drift detection. Extend coverage to HR-specific failure classes: access-boundary violations, cross-scope leakage, jurisdictional and policy-variant correctness.
- Production quality and safety: observability for containment, hallucination rate, tool-selection error, unsafe action, and injection vectors. Design diagnosis that works without exposing restricted HR conversation content.
- AI-assisted engineering standards: convert ambiguous problems into testable specs; define accountable agent-assisted delivery standards for the domain, including specification standards, review expectations, and verification harnesses.
- Hands-on execution: tackle hard integrations, risky migrations, prototypes that settle architectural debates, and incidents that block others.
- Cross-functional partnership: collaborate with product, engineering, and design to co-create scalable AI solutions and translate business needs into robust technical designs.
Requirements:
- 10+ years of software engineering with a record of technical direction adopted beyond your immediate team.
- Direct, hands-on experience authoring agentic instructions and prompts, designing autonomous workflows, and building evaluation that verifies them.
- Production experience with LLM APIs, retrieval-grounded features, agent orchestration, tool and function calling, and structured output enforcement—including at least one model or orchestration migration carried through production.
- Entitlement-aware data handling: per-user access enforcement in retrieval and tool layers, scope separation between roles, and handling of restricted records under audit.
- Evaluation experience used by engineers other than yourself: dataset curation, judge calibration, CI gating, drift detection.
- Strong command of system design, APIs, data modeling, and testing across front-end, server-side, and relational data work with a high quality mindset.
- Effective, accountable use of AI coding assistants and agents.
- On-call and incident-command experience on customer-facing systems, including ownership of systemic fixes that followed.
- Experience with Java, JavaScript/TypeScript, React, and demonstrated ability to quickly learn new tools and frameworks.
- Bachelor's degree in CS, software engineering, or related technical field, or equivalent practical experience.
Preferred:
- HR domain depth: case management, employee journey and lifecycle, or payroll/benefits/leave/absence sufficient to set requirements.
- Integration with core HR, payroll, or benefits systems of record, including reconciliation and eventual-consistency patterns.
- Contribution in a Forward deployment model to champion customer adoption.
