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Salary: USD 240,100 - 420,200 / annual
ServiceNow's Security and Risk Engineering organization is building a new class of AI-powered security solutions focused on exploitability-based exposure analysis. Rather than ranking vulnerabilities by raw severity, this zero-to-one incubation ranks security work by exploitability—where an attacker could realistically gain access. The architecture is still evolving, and this Principal ML Engineer role will help define what good looks like across the portfolio.
In this role, you will set the technical vision and architecture for exploitability-driven security across multiple products and teams. You will define the hardest unsolved modeling problems—such as how calibrated attack-path probability holds up across different environments, how identity and agent surfaces enter the model, and how ground truth feeds back into the system. You will establish engineering standards and architectural direction that multiple teams build within, focusing on scalability, reliability, and scientific rigor of the scoring.
Key responsibilities include:
- Setting technical direction across multiple teams without direct authority, turning ambitious ideas into working, enterprise-grade products
- Exploring and applying emerging AI to cybersecurity in fundamentally new ways—not simply bolting AI onto existing products
- Representing the team's technology and innovation to executives, customers, partners, and the broader engineering organization
- Mentoring staff and senior engineers, raising the overall engineering bar through coaching and technical leadership
- Championing AI-native engineering practices, including extensive use of coding agents and autonomous development, testing, evaluation, and operational workflows
- Setting direction for AI safety, security, governance, and guardrails for agentic systems running in production
- Defining the build-on strategy across the portfolio: what the engine reuses from existing products, what must be net-new, and why
You will work at the intersection of machine learning, security, and enterprise systems, with the opportunity to shape how AI transforms cybersecurity operations at scale.
REQUIREMENTS:
- 15+ years of software engineering experience, including significant technical and engineering leadership responsibility
- Deep expertise in modern AI/ML with a track record of building production AI systems—LLMs, foundation models, agentic architectures, RAG, retrieval, and model evaluation—alongside probabilistic or ML-driven scoring
- Demonstrated experience designing and delivering AI/ML-powered products and platforms in production
- Experience leading technical initiatives spanning multiple teams without direct authority
- Demonstrated ability to design systems that scale to enterprise workloads
- Hands-on experience with frontier LLMs, agent frameworks, retrieval and vector technologies, and model evaluation and observability; probabilistic modeling or graph analytics is a strong plus
- Strong backend engineering experience with distributed systems, APIs, microservices, and cloud-native architectures
- Expert-level Python and modern AI frameworks and infrastructure; experience with Java, Go, or similar languages is valuable
- Extensive experience using AI-native development tools and coding agents (Claude Code, Codex, Cursor, Windsurf, etc.) as part of the software development lifecycle
- Executive-level communication: able to articulate a compelling technical vision to engineers, customers, and senior leadership
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience
- Cybersecurity products, or deep familiarity with modern security architectures and security operations, is strongly preferred
- Primary skills required: Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, and MLOps
