layiq
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Loading LAYIQ…Job opportunity
Santa Clara, California, United States
Source: ServiceNow careers · View original posting
From ServiceNow's posting. “We” and “our” refer to the employer.
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
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.
As a Senior Staff Engineer, you own the architecture of an security harness with novel exploitability engine end to end, and you’re accountable for the decisions that shape everything downstream. You set technical direction, make the hard calls defensible, and multiply the engineers around you.
The end-to-end architecture of the exploitability engine—from evidence ingestion and entity resolution, through the attack-path probability core and choke-point ranking, to the validation loop that keeps predictions honest.
The decisions that cascade through the system: calibrated probability versus ordinal rank, identity as a first-class graph edge, assume-breach seeding, and how the most critical assets are defined. These are model-shaping calls, not implementation details.
The probabilistic ranking core: edge-traversal probability, guided path search with hop and likelihood limits, correlated-control-failure modeling, and honest uncertainty bands.
The calibration and validation loop—canaries, purple-team and incident replay, calibration measured by zone and vector—that turns modeled weights into evidence rather than opinion.
Make-or-break metrics as first-class engineering targets, starting with entity-resolution accuracy and calibration quality.
The build-on strategy—extending the existing portfolio rather than rebuilding it, and knowing precisely what to reuse and what must be net-new.
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