Job at a glance
Company Description It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better.
We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started. Join us to put AI to work for people.   Job Description About the team  The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning.  This is a zero-to-one incubation.
We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like.  The role  As a Principal ML Engineer, you set the technical vision for exploitability-based security across the portfolio—not just one engine.
You define the hardest modeling problems worth solving, set the direction other staff and senior engineers build within, and represent the work to executives, customers, and the broader engineering organization.  What you’ll own  The technical vision and architecture for exploitability-driven security: where the engine goes next, and the class of problems it should solve beyond any single release.  The hardest unsolved modeling problems—how calibrated attack-path probability holds up across environments, how identity and agent surfaces enter the model, and how ground truth feeds back into it.  The engineering standards and architectural direction that multiple teams build within: scalability, reliability, and the scientific rigor of the scoring.  The build-on strategy across the portfolio: what the engine reuses from existing products, what must be net-new, and why.  What you’ll do  Set technical direction across multiple teams without direct authority, and turn ambitious ideas into working, enterprise-grade products.  Explore and apply emerging AI to cybersecurity in fundamentally new ways—not simply bolt AI onto existing products.  Represent the team’s technology and innovation with executives, customers, partners, and the broader engineering organization.  Mentor staff and senior engineers, and raise the overall engineering bar through coaching and technical leadership.  Champion AI-native engineering practices, including extensive use of coding agents and autonomous development, testing, evaluation, and operational workflows.  Set the direction for AI safety, security, governance, and guardrails for agentic systems running in production.  What you bring  Deep expertise in modern AI/ML with a track record of building production AI systems—LLMs, foundation models, agentic architectures, RAG, retrieval, and model evaluation—alongside probabilistic or ML-driven scoring.  The ability to set a compelling technical vision and drive it across teams, then go deep into architecture and code.  A proven record of turning ambitious, ambiguous ideas into products that scale to enterprise workloads.  An innovator’s mindset—challenging conventional approaches and seizing the openings created by rapidly evolving AI.  Command of distributed systems, APIs, cloud-native platforms, and data or graph systems.  Expert-level Python and modern AI frameworks and infrastructure; experience with Java, Go, or similar languages is valuable.  Executive-level communicati