Senior Staff Machine Learning Engineer

• The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect Service Now 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 Senior Staff Engineer, you own the architecture of an security harness with novel exploitability engine end to end, and you’re accountable for the decisions that shape everything downstream
• You set technical direction, make the hard calls defensible, and multiply the engineers around you
• The end-to-end architecture of the exploitability engine—from evidence ingestion and entity resolution, through the attack-path probability core and choke-point ranking, to the validation loop that keeps predictions honest
• The decisions that cascade through the system: calibrated probability versus ordinal rank, identity as a first-class graph edge, assume-breach seeding, and how the most critical assets are defined.
• These are model-shaping calls, not implementation details
• The probabilistic ranking core: edge-traversal probability, guided path search with hop and likelihood limits, correlated-control-failure modeling, and honest uncertainty bands
• The calibration and validation loop—canaries, purple-team and incident replay, calibration measured by zone and vector—that turns modeled weights into evidence rather than opinion
• Make-or-break metrics as first-class engineering targets, starting with entity-resolution accuracy and calibration quality
• The build-on strategy—extending the existing portfolio rather than rebuilding it, and knowing precisely what to reuse and what must be net-new
• Lead zero-to-one work at production scale: turn an ambiguous, novel problem into a reliable system other teams build on, and set the bar where no precedent exists
• Drive technical direction across architecture, design, and code reviews, and raise the engineering bar across the incubation
• Mentor senior engineers and lead by influence, not title
• Partner with product, security R&D, Sec Ops to turn customer problems into architecture, and translate that architecture into decisions leaders can act on
• Establish AI safety, security, governance, and guardrails for agentic systems running in production

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time
• Experience with AI evaluation, safety, governance, or policy guardrails is a plus
• A track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software
• Technical leadership and mentorship that moves teams through influence
• Demonstrated experience as the technical owner or lead for a major system or across teams
• Strong programming experience in Python and/or Java, Go, or a similar language
• Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred
• Command of distributed systems, APIs, cloud-native development, and data or graph systems
• Hands-on depth in both agentic and LLM systems and probabilistic or ML-driven scoring—graph modeling, calibration, search and optimization, or risk and probability engineering
• Proven zero-to-one at scale: you’ve taken an ambiguous problem to a reliable production system that others depend on
• Applied interest in security problems—attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response is strongly preferred
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience
• Expert-level Python, and/or Java, Go, or Type Script
• 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 harness and workflows, model evaluation, or AI observability
• 10+ years of software engineering experience, including leading the design and delivery of complex production systems
• Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus
• Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures
• The judgment to make consequential architecture decisions under uncertainty, and make them defensible to engineers and executives alike.
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