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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 focused on a novel class of exposure analysis that ranks security work by exploitability—where an attacker could realistically gain access—rather than raw severity. This is a foundational architecture project where precedent doesn't exist.
As Senior Staff Machine Learning Engineer, you own the end-to-end architecture of the exploitability engine and are accountable for the technical decisions that shape everything downstream. You set technical direction, make hard calls defensible, and multiply the engineers around you through influence and mentorship.
Key ownership areas include:
- End-to-end architecture of the exploitability engine: evidence ingestion, entity resolution, attack-path probability core, choke-point ranking, and validation loops
- 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 and incident replay, zone and vector-based calibration measurement
- Make-or-break metrics as first-class engineering targets (entity-resolution accuracy, calibration quality)
- Build-on strategy: extending existing portfolio with precision on what to reuse vs. build net-new
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; 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
- Hands-on depth in agentic and LLM systems, probabilistic or ML-driven scoring (graph modeling, calibration, search/optimization, risk/probability engineering)
- Proven zero-to-one at scale: taken ambiguous problems to reliable production systems others depend on
- Judgment to make consequential architecture decisions under uncertainty, defensible to engineers and executives
- Command of distributed systems, APIs, cloud-native development, data/graph systems
- Expert-level Python and/or Java, Go, TypeScript
- Technical leadership and mentorship that moves teams through influence
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical discipline (or equivalent practical experience)
- Primary skills: Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, MLOps
- Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness/workflows, model evaluation, AI observability
- Strongly preferred: Applied interest in security problems (attack-path analysis, vulnerability management, identity security, threat intelligence, detection/response); cybersecurity or security-product experience; familiarity with modern security architectures and operations
- Plus: Experience with AI-assisted development tools (Claude Code, Codex, Cursor, Windsurf); AI evaluation, safety, governance, or policy guardrails
