Senior Machine Learning Engineer

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, Service Now is the AI control tower for business reinvention. Our Service Now 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.

About the team

The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect Service Now 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 architectureis evolving, and this role helps define what good looks like.

The role

As a Senior Staff Engineer, you own the architecture ofansecurity harness withnovel 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.

What you’ll own

• 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 cascadethrough 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 metricsas first-class engineering targets, starting with entity-resolution accuracy and calibration quality.
• The build-on strategy—extending the existing portfolio rather than rebuildingit, andknowing precisely what to reuse and what must be net-new.

What you’ll do

• Lead zero-to-one work at production scale: turn an ambiguous, novel problem into a reliable system other teams buildon, andset the bar where no precedent exists.
• Drive technical direction across architecture, design, and code reviews, and raise the engineering bar across the incubation.
• Mentor senior engineers and lead by influence, not title.
• Partner with product, securityR&D,Sec Opsto turn customer problems into architecture, and translate that architecture into decisions leaders can act on.
• Establish AI safety, security, governance, and guardrails for agentic systems running in production.

What you bring

• A track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software.
• Hands-on depth in both agentic and LLM systems and probabilistic or ML-driven scoring—graph modeling, calibration, search and optimization, or risk and probability engineering.
• Proven zero-to-one at scale: you’ve taken an ambiguous problem to a reliable production system that others depend on.
• The judgment to make consequential architecture decisions…