Senior ML Engineer

ServiceNow•Santa Clara, CA

About The Position

As a Senior ML Engineer in ServiceNow’s Security and Risk Engineering organization, you will build core components of a novel exploitability engine that turns raw security signals into ranked, reachable attack paths. The team is incubating a new approach to exposure analysis that prioritizes security work based on realistic exploitability rather than raw severity. The architecture is evolving, and you will help shape it. You will take well-scoped problems from design through production, with opportunities to assume deeper ownership as the system matures. Your work may include evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core. You will be accountable for the correctness and reliability of your contributions, including testing, evaluation, observability, and relevant metrics. You will also contribute to the data and model plumbing that keeps the graph accurate, including entity-resolution quality, evidence provenance, and decay. The role involves designing, building, testing, and operating production ML components; collaborating with product, security R&D, and SecOps; evaluating AI capabilities against cybersecurity problems; and applying AI safety, security, and guardrail practices. The position requires at least 3 years of software engineering experience or equivalent practical experience. A bachelor's or master's degree in a relevant technical discipline is listed, or equivalent practical experience.

Requirements

At least 3 years of software engineering experience, or equivalent practical experience.
Solid software engineering fundamentals and experience operating production-quality software.
Experience designing and delivering production software systems.
Hands-on experience building ML- or LLM-powered applications, such as RAG, embeddings, agents, or probabilistic or ML-driven scoring.
Experience taking a prototype to a reliable, maintainable production solution.
Working knowledge of distributed systems, APIs, cloud-native development, and data or graph systems.
Strong programming experience in Python and/or Java, Go, TypeScript, or a similar language.
Experience building or integrating AI/ML-powered applications in a production or near-production environment.
Modern AI experience with LLMs, RAG, embeddings, vector search, agentic 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

Interest or experience in security problems such as vulnerability management, identity security, threat detection, or risk prioritization.
Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf.
Experience with AI evaluation, safety, or guardrails.
Cybersecurity or security-product experience.

Responsibilities

Build production-quality components of the exploitability engine, such as evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core.
Ensure the correctness and reliability of shipped components through tests, evaluation, observability, and metrics.
Turn ambiguous requirements into working code, with guidance on decisions affecting the wider system.
Contribute to the data and model plumbing that maintains graph accuracy, including entity-resolution quality, evidence provenance, and decay.
Design, build, test, and operate production ML components using strong engineering fundamentals.
Contribute to design and code reviews and help raise the team's quality bar.
Prototype and evaluate new AI capabilities against real cybersecurity problems.
Partner with product, security R&D, and SecOps to understand underlying problems.
Apply AI safety, security, and guardrail practices to developed solutions.