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Salary: USD 176,100 - 308,200 / annual
ServiceNow's Security and Risk Engineering organization is building a new class of AI-powered exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. This is a zero-to-one incubation with an evolving architecture.
As a Staff ML Engineer, you will own a major subsystem of a novel exploitability engine end-to-end. This could be the probability core, the exposure graph and entity-resolution layer, or the calibration and validation loop. You will make design decisions within your area, drive them to production, and define what good architecture looks like.
Key responsibilities:
- Own a major subsystem end-to-end, including design, delivery, and quality
- Make design decisions within your subsystem and define how it interfaces with the rest of the engine
- Establish and own metrics that prove your subsystem works (e.g., entity-resolution accuracy, calibration quality, path-ranking precision)
- Provide technical direction for engineers working in your area
- Take an ambiguous subsystem from design through production at scale
- Drive design and code reviews, raising the engineering bar around you
- Mentor engineers and lead workstreams through influence
- Partner with product, security R&D, and SecOps to turn customer problems into subsystem design
- Establish AI safety, security, and guardrails for agentic components
You will work on a system that helps ServiceNow and its customers reduce security risk through AI-first thinking, clean architecture, and intuitive experiences.
REQUIREMENTS:
- 6+ years of software engineering experience, including leading the design and delivery of complex production components
- 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)
- Primary skills: Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, and MLOps
- Proven delivery of ambiguous problems to reliable production systems that others depend on
- Sound judgment for design decisions under uncertainty
- 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 workstreams through influence
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline, or equivalent practical experience
- Cybersecurity or security-product experience, or familiarity with modern security architectures and operations (strongly preferred)
- Applied interest in security problems (attack-path analysis, vulnerability management, identity security, threat intelligence, detection and response) is preferred
- Experience with AI-assisted development tools (Claude Code, Codex, Cursor, Windsurf) is a plus
- Experience with AI evaluation, safety, governance, or policy guardrails is a plus
