Senior ML Engineer

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started. Join us to put AI to work for people.

About the team The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning. This is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like.

As a Senior ML Engineer, you build core components of a novel exploitability engine—shipping production ML that turns raw security signal into ranked, reachable attack paths. You take well-scoped problems from design to production and grow into deeper ownership as the system matures. What you’ll own Well-scoped components of the engine—evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core—built to production quality. The correctness and reliability of what you ship: tests, evaluation, observability, and the metrics that show your component works. Turning ambiguous requirements into working code, with guidance on the calls that shape the wider system. The data and model plumbing that keeps the graph accurate—entity-resolution quality, evidence provenance, and decay. What you’ll do Design, build, test, and operate production ML components with strong engineering fundamentals. Contribute to design and code reviews, and help raise 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 the problem behind the ticket. Apply AI safety, security, and guardrail practices to what you build. What you bring Solid software engineering fundamentals and experience operating production-quality software. Hands-on experience building ML- or LLM-powered applications—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 Python, and/or Java, Go, or TypeScript. Interest in security problems—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 is a plus. Experience with AI evaluation, safety, or guardrails is a plus.

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: 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. Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures.