Senior Staff Machine Learning Engineer

ServiceNow•Santa Clara, CA

4d•$201,300 - $352,300

About The Position

The Senior Staff Machine Learning Engineer will own the end-to-end architecture of a new security harness and exploitability engine within ServiceNow’s Security and Risk Engineering organization. This zero-to-one incubation aims to rank security work according to realistic exploitability rather than raw severity. The role will help define the architecture and technical standards for the system, from evidence ingestion and entity resolution through probabilistic attack-path analysis, choke-point ranking, and validation. The engineer will set direction for consequential modeling and architecture decisions, including probability calibration, identity representation, assume-breach seeding, critical-asset definitions, correlated control failures, and uncertainty. They will establish validation practices using canaries, purple-team exercises, and incident replay, and make key metrics such as entity-resolution accuracy and calibration quality first-class engineering targets. The role also involves extending the existing product portfolio where appropriate and identifying what needs to be built anew. The position leads ambiguous technical work into reliable production systems, guides architecture, design, and code reviews, and mentors senior engineers through influence. It partners with product, security R&D, and SecOps, and establishes AI safety, security, governance, and guardrails for production agentic systems.

Requirements

10 or more years of software engineering experience, including leading the design and delivery of complex production systems.
Track record owning architecture across a large system or multiple teams and operating production-quality software.
Demonstrated experience as the technical owner or lead for a major system or across teams.
Hands-on depth in agentic and LLM systems and probabilistic or ML-driven scoring, such as graph modeling, calibration, search and optimization, or risk and probability engineering.
Experience taking an ambiguous problem to a reliable production system at scale that others depend on.
Strong judgment in making consequential architecture decisions under uncertainty and explaining them to engineers and executives.
Command of distributed systems, APIs, cloud-native development, and data or graph systems.
Expert-level Python, and/or Java, Go, or TypeScript or a similar language.
Technical leadership and mentorship that moves teams through influence.
Experience building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus.
Modern AI experience, such as LLMs, RAG, embeddings, vector search, agentic harnesses and workflows, model evaluation, or AI observability.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.

Nice To Haves

Applied interest or experience in security problems such as attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response.
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations.
Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf.
Experience with AI evaluation, safety, governance, or policy guardrails.
Depth in probabilistic modeling, graph analytics, calibration, and evaluation.

Responsibilities

Own the end-to-end architecture of the exploitability engine, from evidence ingestion and entity resolution to attack-path probability analysis, choke-point ranking, and validation.
Set and defend consequential modeling and architecture decisions, including probability versus ordinal ranking, identity graph representation, assume-breach seeding, and critical-asset definitions.
Design the probabilistic ranking core, including edge-traversal probability, guided path search, correlated-control-failure modeling, and uncertainty bands.
Build the calibration and validation loop using canaries, purple-team exercises, incident replay, and calibration measured by zone and vector.
Establish make-or-break engineering metrics, including entity-resolution accuracy and calibration quality.
Determine how to extend the existing portfolio, including what to reuse and what must be built new.
Lead zero-to-one engineering work at production scale, turning ambiguous problems into reliable systems other teams can build on.
Drive technical direction across architecture, design, and code reviews, and raise engineering standards across the incubation.
Mentor senior engineers and lead through influence.
Partner with product, security R&D, and SecOps to translate customer problems into architecture and communicate actionable decisions to leaders.
Establish AI safety, security, governance, and guardrails for agentic systems in production.