Staff AI Engineer (Conversational & Agentic AI)

ServiceNow; Santa Clara (CA); On-site; USD 190,000 - 290,000; Full time

Benefits offered by this job

Generous family leave

Matched donations

Annual learning stipends

Flexible PTO

Competitive retirement plan

Paid volunteer time

Job summary

ServiceNow in Santa Clara is seeking a Staff AI Engineer to own the agent harness infrastructure, enabling enterprise AI to reason over real data, run workflows, and scale safely.

You will design the orchestration layer, improve reliability and observability, and shape prompt management and evaluation frameworks across model updates.

Responsibilities

• Own the agent harness infrastructure for enterprise AI agents.
• Design and build the orchestration layer for multi-agent workflows.
• Ensure reliability, latency, and throughput in production environments.
• Instrument observability with tracing, cost attribution, and latency visibility.

Job description

• The Agentic Engineering organization at ServiceNow is the customer-obsessed engineering group that builds a conversational AI experience that turns enterprise intent into completed work
• We advance how enterprise AI reasons, remembers, and executes
• The Agent Orchestration team — the team you’ll join — owns the execution core: the agent harness, orchestration runtime, multi‑agent coordination, memory management, and the evaluation frameworks that ensure agents behave correctly in production
• Every autonomous action Otto promises depends on what this team ships
• By joining our team, you’ll be at the forefront of our AI transformation journey, backed by the global scale of ServiceNow and the agility of a high‑growth environment
• We are looking for world‑class talent to help us extend agentic AI to every employee across every corner of the business
• As a Staff AI Engineer, you will own significant parts of the agent harness — the infrastructure layer that enables AI agents to reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale
• Harness engineering: Design and build the agent execution harness — the orchestration layer that routes inputs, manages context, invokes tools, handles retries, and surfaces execution state across multi‑step agentic workflows
• Reliability at scale: Own the runtime’s fault tolerance, latency, and throughput; design for enterprise workflows that cannot fail silently or unpredictably
• Observability: Instrument the harness with tracing, cost attribution, and latency visibility so the team can reason about agent behavior in production and catch failures before customers do
• Prompt infrastructure: Build prompt management systems — versioning, templating, and systematic evaluation — that keep agent behaviour stable across model updates and configuration changes
• Eval engineering: Design and own evaluation frameworks (unit evals, integration evals, production monitors) that measure agent quality, catch regressions, and drive data‑informed decisions
• LLM integration: Integrate with and abstract over frontier LLMs, managing model routing, fallback strategies, cost, and latency trade – offs in production
• Technical leadership: Set technical standards through architecture decisions, code reviews, and coaching — particularly on agentic design patterns and production AI discipline
• System boundary design: Define where agent logic lives — what’s a tool call, a sub‑agent, a hardcoded path, or a human escalation — and establish those design standards across the team

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time