Distinguished Platform Engineer

ServiceNow•Santa Clara, CA

1d•$279,100 - $488,400

About The Position

As a Principal Engineer in HRSD, set the technical direction for model-driven capabilities across the HR domain, including agentic and conversational experiences that interpret employee, manager, and HR agent intent, reason over relevant context, and act on users’ behalf. The role owns architecture, standards, and evaluation infrastructure that enable multiple teams to deliver these experiences safely, rather than owning a single feature set. This is not an ML research role and does not involve training foundation models. It is also distinct from traditional deterministic full-stack engineering. The work requires engineering natural-language instructions, prompts, context, tool descriptions, and guardrails with the discipline applied to code, and validating probabilistic behavior through evaluation at scale. The role sets domain AI architecture and multi-release decisions; defines safe autonomy boundaries for HR actions; and owns the shared, versioned instruction and tool-description surface that customers configure and extend. It establishes evaluation infrastructure, production quality and safety practices, and standards for AI-assisted engineering. The engineer also contributes hands-on to difficult integrations, risky migrations, prototypes, and incidents, and partners with product, engineering, and design to create scalable AI solutions.

Requirements

10+ years of software engineering experience, with a record of technical direction adopted beyond the immediate team.
Hands-on experience authoring agentic instructions and prompts, designing autonomous workflows, and building evaluations to verify them.
Production experience with LLM APIs, retrieval-grounded features, agent orchestration, tool and function calling, and structured-output enforcement.
Experience carrying at least one model or orchestration migration through production.
Experience with entitlement-aware data handling, including per-user access enforcement in retrieval and tool layers, role-based scope separation, and audited handling of restricted records.
Evaluation experience used by engineers other than the candidate, including dataset curation, judge calibration, CI gating, and 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 and accountable use of AI coding assistants and agents.
On-call and incident-command experience on customer-facing systems, including ownership of systemic follow-up fixes.
Experience with Java, JavaScript/TypeScript, and React, and demonstrated ability to learn new tools and frameworks quickly.
Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

HR domain depth in case management, employee journeys and lifecycle, or payroll, benefits, leave, or absence, sufficient to set requirements.
Experience integrating 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.

Responsibilities

Set the AI architecture for the HR domain, including agent decomposition and composition, reasoning placement, context assembly and limits, tool exposure, and delegation of autonomy.
Own multi-release technical decisions covering model selection and migration, orchestration, build-versus-adopt choices, and cost, latency, and quality tradeoffs.
Define which HR actions agents may take, which require human decision points, and which agents must not attempt; build structural safeguards for sensitive actions and matters.
Own the shared instructions and tool-description surface as a versioned product contract, including upgrade-safe extension points, deprecation paths, and compatibility guarantees.
Build and maintain evaluation infrastructure, including golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection.
Extend evaluation coverage to HR-specific failure classes, including access-boundary violations, cross-scope leakage, and jurisdictional or policy-variant correctness.
Establish production observability and safety practices for containment, hallucination rates, tool-selection errors, unsafe actions, and prompt-injection vectors, while protecting restricted HR conversation content.
Define AI-assisted engineering standards, including specifications, review expectations, and verification harnesses, and ensure teams follow them.
Provide hands-on leadership for difficult integrations, risky migrations, architectural prototypes, and incidents that require unblocking.
Collaborate with product, engineering, and design to co-create scalable AI solutions and translate business needs into technical designs.
Participate in on-call and incident command for customer-facing systems and own systemic fixes following incidents.