Staff Machine Learning Engineer

ServiceNow•Santa Clara, CA

6d•$176,100 - $308,200•Hybrid

About The Position

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 Staff ML Engineer, you own a major subsystem of a novel exploitability engine end to end—for example the probability core, the exposure graph and entity-resolution layer, or the calibration and validation loop. You make the design calls within your area and drive them to production.

Requirements

A track record of owning a significant system or subsystem end-to-end in production.
Hands-on depth in agentic and LLM systems and/or probabilistic or ML-driven scoring—graph modeling, calibration, search and optimization.
Proven delivery of an ambiguous problem to a reliable production system that others depend on.
The judgment to make sound design decisions under uncertainty within your area.
Command of distributed systems, APIs, cloud-native development, and data or graph systems.
Expert-level Python, and/or Java, Go, or TypeScript.
Technical leadership that moves a workstream through influence.
6+ years of software engineering experience, including leading the design and delivery of complex production components.
Demonstrated experience as the technical owner or lead for a significant system or subsystem.
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 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.

Nice To Haves

Applied interest in security problems—attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response—is 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.
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred.

Responsibilities

Own a major subsystem end-to-end—the probability core, the exposure graph and entity resolution, or the calibration and validation loop—including its design, delivery, and quality.
Make design decisions within your subsystem, and how it interfaces with the rest of the engine.
Own the metrics that prove your subsystem works—entity-resolution accuracy, calibration quality, or path-ranking precision—as first-class targets.
Provide technical direction for the engineers working in your area.
Take a major, ambiguous subsystem from design through production at scale.
Drive design and code reviews in your area, and raise the engineering bar around you.
Mentor engineers and lead a workstream through influence.
Partner with product, security R&D, and SecOps to turn customer problems into subsystem design.
Establish AI safety, security, and guardrails for the agentic parts of your subsystem.