Senior ML Engineer

ServiceNow•Santa Clara, CA

3d•$143,200 - $243,400

About The Position

The Senior ML Engineer joins ServiceNow’s Security and Risk Engineering organization to build production machine-learning components for a new exploitability engine. The engine analyzes security evidence to rank work based on realistic exploitability and reachable attack paths, rather than raw severity. This is a zero-to-one incubation with an evolving architecture, and the role helps shape its technical direction while taking well-scoped problems from design through production and growing into deeper ownership as the system matures. The engineer may own components such as evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core. The role is responsible for the correctness and reliability of shipped components, including tests, evaluation, observability, and metrics. It also involves maintaining the data and model plumbing behind an accurate graph, including entity-resolution quality, evidence provenance, and decay. The position designs, builds, tests, and operates production ML components; contributes to design and code reviews; prototypes AI capabilities against cybersecurity problems; and partners with product, security R&D, and SecOps. The engineer applies AI safety, security, and guardrail practices to their work.

Requirements

At least 3 years of software engineering experience, or equivalent practical experience.
Experience designing and delivering production software systems.
Experience building or integrating AI/ML-powered applications in a production or near-production environment.
Modern AI experience, such as LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
Strong programming experience in Python and/or Java, Go, or a similar language.
Working knowledge of distributed systems, APIs, cloud-native development, and data or graph systems.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.
Solid software engineering fundamentals and experience operating production-quality software.
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.

Nice To Haves

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

Responsibilities

Design, build, test, and operate production ML components with strong engineering fundamentals.
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.
Maintain data and model plumbing for graph accuracy, including entity-resolution quality, evidence provenance, and decay.
Contribute to design and code reviews and help raise the quality bar on the team.
Prototype 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 systems.