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Salary: USD 201,300 - 352,300 / annual
ServiceNow's Security and Risk Engineering organization is building a zero-to-one incubation for a novel exploitability engine—a new class of security exposure analysis that ranks vulnerabilities by realistic attack likelihood rather than raw severity.
As Senior Staff Machine Learning Engineer, you own the end-to-end architecture of this exploitability engine and are accountable for the technical decisions that shape everything downstream. This is a high-impact role where you set technical direction, make defensible hard calls, and multiply the engineers around you.
Key ownership areas:
- End-to-end architecture: evidence ingestion, entity resolution, attack-path probability core, choke-point ranking, and validation loops that keep predictions honest
- Model-shaping decisions: calibrated probability vs. ordinal rank, identity as graph edges, assume-breach seeding, critical-asset definition
- Probabilistic ranking core: edge-traversal probability, guided path search with constraints, correlated-control-failure modeling, uncertainty quantification
- Calibration and validation: canaries, purple-team exercises, incident replay, zone and vector-based calibration
- Make-or-break metrics as first-class engineering targets (entity-resolution accuracy, calibration quality)
- Build-on strategy: extending existing portfolio with precision about what to reuse vs. rebuild
You will lead zero-to-one work at production scale, turning ambiguous novel problems into reliable systems others depend on. You'll drive technical direction across architecture, design, and code reviews; mentor senior engineers through influence; partner with product, security R&D, and SecOps to translate customer problems into architecture; and establish AI safety, security, governance, and guardrails for agentic systems in production.
Requirements:
- 10+ years of software engineering experience, including leading design and delivery of complex production systems
- Demonstrated experience as technical owner or lead for a major system or across teams
- Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, calibration and evaluation strongly preferred
- Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, AI observability
- Strong programming in Python and/or Java, Go, or similar language
- Cloud-native technologies, distributed systems, APIs, databases, scalable architectures
- Bachelor's or Master's degree in Computer Science, AI, 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
- Track record of owning architecture across large systems with deep production-quality software experience
- Hands-on depth in agentic and LLM systems and probabilistic/ML-driven scoring
- Judgment to make consequential architecture decisions under uncertainty
- Technical leadership and mentorship that moves teams through influence
- Applied interest in security problems (attack-path analysis, vulnerability management, identity security, threat intelligence, detection/response) strongly 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
