Staff Machine Learning Engineer

ServiceNow•Santa Clara, CA

2d•$176,100 - $308,200

About The Position

The Staff Machine Learning Engineer works in ServiceNow’s Security and Risk Engineering organization, which builds scalable, AI-powered security solutions to reduce risk and protect the company and its customers. This is a zero-to-one incubation effort to develop an exposure-analysis engine that prioritizes security work by realistic exploitability rather than raw severity. The role owns a major subsystem of the 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 for their area, delivers the subsystem to production, defines how it interfaces with the wider engine, and owns the metrics used to assess its quality. The role also provides technical direction, mentors engineers, and partners with product, security R&D, and SecOps to translate customer problems into subsystem designs. It establishes safety, security, and policy 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.
A track record of delivering ambiguous problems as reliable production systems 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.
Experience building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation experience is relevant.
Modern AI experience such as RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
Command of distributed systems, APIs, cloud-native development, and data or graph systems, including databases and scalable architectures.
Expert-level Python and/or strong programming experience in Java, Go, or a similar language.
Technical leadership capable of moving a workstream forward 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

Applied interest in security problems such as attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response.
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations.
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.
Depth in probabilistic modeling, graph analytics, or calibration and evaluation.

Responsibilities

Own a major exploitability-engine subsystem end to end, including its design, delivery, and quality.
Make design decisions within the subsystem and determine how it interfaces with the rest of the engine.
Own first-class metrics for subsystem performance, such as entity-resolution accuracy, calibration quality, or path-ranking precision.
Take an ambiguous subsystem from design through production at scale.
Drive design and code reviews and raise engineering standards in the area.
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 components.
Provide technical direction for engineers working in the subsystem.