It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
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.
What they're looking for
- 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
More about this role
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 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.
What you’ll 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.
- The design decisions within your subsystem, and how it interfaces with the rest of the engine.
- The metrics that prove your subsystem...
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