Principal Machine Learning Engineer

ServiceNow•San Diego, CA

2d•$216,100 - $378,200•Hybrid

About The Position

Enterprise AI is ServiceNow's horizontal capability team, building the operating model, technical foundation, and Customer Zero motion for AI governance at enterprise scale, including AI Control Tower, the AI agent orchestrator, and the AI agent fabric on the ServiceNow AI platform. As Principal Engineer, Agentic AI Systems, you will serve as the senior-most technical multiplier for the Enterprise AI organization, setting architecture and technical direction for one of ServiceNow's biggest engineering opportunities: making an enterprise-scale workforce of AI agents governable, secure, observable, and worth trusting.

Requirements

15+ years of software engineering experience, including 5+ years operating at a principal, senior staff, or director-equivalent scope, setting technical strategy across multi-team organizations without relying on direct reporting authority.
Deep, current, hands-on expertise in agentic AI and LLM-based systems, multi-agent orchestration, RAG, evaluation frameworks, LLMOps/MLOps, AI observability, and AI governance and security.
Experience architecting enterprise-scale cloud platforms, distributed systems, developer platforms, data platforms, or DevOps/CI-CD automation.
A demonstrated track record of transforming how engineering organizations build, test, and ship software, ideally with AI-assisted or agentic coding tools.
Strong judgment in ambiguous technical domains where architecture, product, security, operations, and business value intersect.
Builder credibility: you still write code, prototype fast, and lead by demonstration.
Executive communication skills, with the ability to frame technical strategy and trade-offs for VP, C-level, customer, pundits, and partner audiences.

Nice To Haves

Experience with ServiceNow or a comparable enterprise workflow platform (ITSM, CMDB, GRC).
Experience with MCP, A2A, tool-calling architectures, or LLM/AI gateways.
Visible technical guidance through publications, talks, patents, or open-source contributions.

Responsibilities

Define the multi-year technical architecture for the AI Control Tower portfolio, agent governance, orchestration, lifecycle management, interoperability, observability, evaluation, and AI security, as one coherent platform.
Establish an AI-forward developer experience that embeds agents into planning, coding, testing, review, deployment, and operations, with the standards and guardrails to make it safe and measurable.
Define operational excellence for autonomous and semi-autonomous agents, including reliability, incident response, rollback, kill-switch, and auditability standards.
Act as the technical multiplier across AI engineering, data and ML, infrastructure, product, DevOps, security, and GRC, brokering cross-domain choices and resolving design conflicts on technical merit.
Turn ServiceNow's own Customer Zero experience into structured product feedback for AI Control Tower and related platform capabilities.
Prototype and incubate new patterns in multi-agent orchestration, agent evaluation, and AI governance automation, and publish reference architectures that accelerate reuse.
Mentor principal and senior engineers, raise hiring standards, and build the AI fluency that makes this team a model for AI-ready engineering.