Asks for

The ad says“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.”
The ad says“Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability.”
The ad says“Command of distributed systems, APIs, cloud-native development, and data or graph systems.”
The ad says“Command of distributed systems, APIs, cloud-native development, and data or graph systems.”
The ad says“Command of distributed systems, APIs, cloud-native development, and data or graph systems.”
The ad says“Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability.”
The ad says“Expert-level Python, and/or Java, Go, or TypeScript.”
The ad says“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.”
The ad says“Expert-level Python, and/or Java, Go, or TypeScript.”
The ad says“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.”
The ad says“Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus.”
The ad says“Expert-level Python, and/or Java, Go, or TypeScript.”
The ad says“Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability.”
The ad says“Expert-level Python, and/or Java, Go, or TypeScript.”
The ad says“Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability.”

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 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.

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 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.

What you’ll do

Lead zero-to-one work at production scale: turn an ambiguous, novel problem into a reliable system other teams build on, and set 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, security R&D, SecOps to 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 under uncertainty, and make them defensible to engineers and executives alike.
Command of distributed systems, APIs, cloud-native development, and data or graph systems.
Expert-level Python, and/or Java, Go, or TypeScript.
Technical leadership and mentorship that moves teams through influence.
Applied interest in security problems—attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response—is strongly preferred.
Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus.
Experience with AI evaluation, safety, governance, or policy guardrails is a plus.

Qualifications

10+ years of software engineering experience, including leading the design and delivery of complex production systems.
Demonstrated experience as the technical owner or lead for a major system or across teams.
Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus.
Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability.
Strong programming experience in Python and/or Java, Go, or a similar language.
Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred.

For positions in this location, we offer a base pay of $201,300 - $352,300, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Additional Information

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. 

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.