SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 143,200 - 243,400 / annual
ServiceNow's Security and Risk Engineering organization is building a zero-to-one incubation: a novel exploitability engine that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. This is an AI-first, architecture-defining effort.
As a Senior ML Engineer, you will build core components of this engine, shipping production ML that transforms raw security signals into ranked, reachable attack paths. You'll take well-scoped problems from design to production and grow into deeper ownership as the system matures.
Key responsibilities:
- Own well-scoped components of the exploitability engine: evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core, built to production quality.
- Ensure correctness and reliability through comprehensive testing, evaluation, observability, and metrics that demonstrate your component works.
- Convert ambiguous requirements into working code, with guidance on architectural decisions that shape the wider system.
- Manage the data and model plumbing that keeps the graph accurate—entity-resolution quality, evidence provenance, and decay.
- Design, build, test, and operate production ML components with strong engineering fundamentals.
- Contribute to design and code reviews, raising the quality bar on the team.
- Prototype quickly to evaluate new AI capabilities against real cybersecurity problems.
- Partner with product, security R&D, and SecOps to understand problems behind tickets.
- Apply AI safety, security, and guardrail practices to your work.
You'll work in a culture that values AI-first thinking, clean architecture, intuitive experiences, and continuous learning.
Requirements:
- 3+ years of software engineering experience, or equivalent practical experience.
- Primary skills: Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, and MLOps.
- Experience designing and delivering production software systems.
- Experience building or integrating AI/ML-powered applications in production or near-production environments.
- Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
- Strong programming in Python and/or Java, Go, or similar languages.
- Knowledge of cloud-native technologies, distributed systems, APIs, databases, and scalable architectures.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline, or equivalent practical experience.
- Cybersecurity or security-product experience is a plus.
- Experience with AI-assisted development tools (Claude Code, Codex, Cursor, Windsurf) is a plus.
- Experience with AI evaluation, safety, or guardrails is a plus.
