Principal Software Engineer

ServiceNow; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

ServiceNow is seeking a Principal Engineer in HRSD to set the technical direction for model-driven capabilities across HR, shaping agent-based experiences and safety-focused infrastructure. You will own architecture, assessment, and orchestration across multi-release model calls, ensuring production-grade, policy-compliant behavior.

The role emphasizes probabilistic behavior with evaluation at scale, integration of agentic prompts, and ownership of shared instruction surfaces and governance

Qualifications

• 10+ years of software engineering with technical direction
• Experience authoring agentic instructions and prompts
• Production experience with LLM APIs, agent orchestration, tool and function calling
• Entitlement-aware data handling and per-user access enforcement
• Evaluation practices: dataset curation, judge calibration, CI gating, drift detection
• Strong system design, APIs, data modeling, and testing across front-end, server-side, and data layers
• On-call and incident-command experience on customer-facing systems
• Experience with Java, JavaScript/TypeScript, React
• Bachelor's degree in CS or related field

Responsibilities

• Define AI architecture for the HRSD domain and how agents reason and operate
• Decide autonomy boundaries and human-in-the-loop requirements
• Shape shared instruction and tool surfaces with versioned contracts
• Own evaluation infrastructure: datasets, multi-turn suites, CI gates, drift detection
• Ensure production safety: observability, containment, and policy-compliant behavior

Skills

Software architecture

Agentic prompts

LLM deployment

System design

Java

TypeScript/React

On-call / incident response

Data access controls

Learning new tools

Education

Bachelor's degree in CS or related field

Tools

Java

TypeScript

React

LLM APIs

Retrieval augmented

CI/CD

Job description

The Role

As a Principal Engineer in HRSD, you set the technical direction for model-driven capability across the HR domain, the agentic and conversational experiences that interpret employee, manager, and HR agent intent, reason over profile, case, catalog, policy, and knowledge context, and act on the user's—back. Your unit of ownership is the architecture, standards, and evaluation infrastructure that let multiple teams ship those experiences safely - not a single feature set.

This is not an ML research role; you do not train foundation models. It is also distinct from traditional full-stack staff work, where systems follow deterministic logic. Two things shape the job: much of the load-bearing logic lives in natural language - instructions, prompts, context, tool descriptions, guardrails - and must be engineered with the same discipline as code; and because behavior is probabilistic, correctness is established through evaluation at scale, not fixed assertions.

What You'll Own

AI architecture for the domain. How agents are decomposed and composed, where reasoning happens, how context is assembled and bounded, how tools are exposed, and how autonomy is delegated. Own the multi-release calls: model selection and migration, orchestration approach, build-versus-adopt, and the cost/latency/quality tradeoffs behind each.

The autonomy boundary. Decide as domain policy which HR actions an agent may take, which require a human decision point, and which no agent should attempt. Anything changing pay, employment status, restricted records, or employee-relations matters requires a human in the path by construction - build the mechanisms that make those constraints structural.

The shared instruction and tool description surface. HRSD ships as product; customers configure, extend, and override this surface on their own instances. Treat it as a versioned contract with upgrade-safe extension points, deprecation paths, and compatibility guarantees.

Evaluation as infrastructure. Own the golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection that let teams change behavior safely. 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 from user-supplied content entering agent context. Because HR conversation content is itself restricted, design diagnosis that works without exposing what was said.

AI-assisted engineering standards. Convert ambiguous problems into testable specs; define what accountable agent-assisted delivery looks like for the domain - specification standards, review expectations, verification harnesses - and hold the line on it.

Hands-on where it matters. The hard integration, the risky migration, the prototype that settles an architectural argument, the incident nobody else can unblock.

Partner across functions. Collaborate efficiently with product, engineering, design to co-create scalable AI solutions and translate business needs into robust technical designs.

What You Bring

• 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 the 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 the systemic fixes that followed.


• Experience in software stack including Java, JavaScript/TypeScript, React and demonstrated ability to quickly learn new tools and frameworks.


• Bachelor's degree in CS, software engineering, or a 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.

JV21

For positions in this location, we offer a base pay o