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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. This is a zero-to-one incubation focused on exploitability-based exposure analysis—ranking security work by where attackers could realistically get in, rather than raw severity.
As Principal ML Engineer, you will set the technical vision for exploitability-driven security across the portfolio. You'll define the hardest modeling problems worth solving, establish architectural direction for multiple teams, and represent this work to executives and customers.
Key responsibilities:
- Own the technical vision and architecture for exploitability-driven security, including where the engine goes next and the class of problems it should solve.
- Tackle the hardest unsolved modeling problems: how calibrated attack-path probability holds up across environments, how identity and agent surfaces enter the model, and how ground truth feeds back into it.
- Set engineering standards and architectural direction that multiple teams build within, ensuring scalability, reliability, and scientific rigor of scoring.
- Define the build-on strategy across the portfolio: what the engine reuses from existing products and what must be net-new.
- Set technical direction across multiple teams without direct authority and turn ambitious ideas into working, enterprise-grade products.
- Explore and apply emerging AI to cybersecurity in fundamentally new ways—not simply bolting AI onto existing products.
- Represent the team's technology and innovation with executives, customers, partners, and the broader engineering organization.
- Mentor staff and senior engineers, raising the overall engineering bar through coaching and technical leadership.
- Champion AI-native engineering practices, including extensive use of coding agents and autonomous development, testing, evaluation, and operational workflows.
- Set the direction for AI safety, security, governance, and guardrails for agentic systems running in production.
The role values AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning. You'll work in a distributed environment with flexible work personas.
Requirements:
- 15+ years of software engineering experience, including significant technical and engineering leadership responsibility.
- 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.
- Extensive experience using AI-native development tools and coding agents (Claude Code, Codex, Cursor, Windsurf, etc.) as part of the software development lifecycle.
- 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.
- Expert-level Python and modern AI frameworks and infrastructure; experience with Java, Go, or similar languages is valuable.
- Executive-level communication: able to articulate a compelling technical vision to engineers, customers, and senior leadership.
