Staff Machine Learning Engineer

ServiceNow•Santa Clara, CA

About The Position

ServiceNow’s Security and Risk Engineering organization builds scalable, AI-powered security solutions to reduce risk and protect ServiceNow and its customers. This role is part of a zero-to-one incubation effort developing exposure analysis that prioritizes security work by realistic exploitability rather than raw severity. The Staff Machine Learning Engineer owns a major subsystem of a novel exploitability engine end to end, such as the probability core, exposure graph and entity-resolution layer, or calibration and validation loop. The engineer makes design decisions within that area, delivers the subsystem to production, defines its interfaces, and owns metrics that demonstrate its quality. The role also provides technical direction, leads through influence, mentors engineers, and partners with product, security R&D, and SecOps to translate customer problems into subsystem design. The engineer establishes AI safety, security, and guardrails for agentic components.

Requirements

At least 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.
Track record of taking an ambiguous problem to a reliable production system that others depend on.
Hands-on depth in agentic and LLM systems and/or probabilistic or ML-driven scoring, including areas such as graph modeling, calibration, search, or optimization.
Expert-level Python and/or strong programming experience in Java, Go, TypeScript, or a similar language.
Command of distributed systems, APIs, cloud-native development, and data or graph systems; experience with databases and scalable architectures.
Technical leadership that moves a workstream through influence.
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

Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation experience is a strong plus.
Modern AI experience with RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations; this is strongly preferred.
Applied interest in security problems such as attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response.
Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf.
Experience with AI evaluation, safety, governance, or policy guardrails.

Responsibilities

Own a major exploitability-engine subsystem from design through production at scale, including its design, delivery, and quality.
Make design decisions within the subsystem and define how it interfaces with the rest of the engine.
Own first-class metrics that demonstrate subsystem performance, such as entity-resolution accuracy, calibration quality, or path-ranking precision.
Drive design and code reviews and raise the engineering bar in the area.
Provide technical direction, mentor engineers, and lead a workstream through influence.
Partner with product, security R&D, and SecOps to translate customer problems into subsystem design.
Establish AI safety, security, and guardrails for agentic parts of the subsystem.