Senior ML Engineer

Join ServiceNow's Security and Risk Engineering organization as a Senior ML Engineer. In this role, you will build core components of a novel exploitability engine, turning raw security signals into ranked, reachable attack paths. You will be responsible for the correctness and reliability of your work, turning ambiguous requirements into working code, and designing, building, testing, and operating production ML components. You will also contribute to design and code reviews, prototype quickly to evaluate new AI capabilities, and apply AI safety, security, and guardrail practices to your work.

• Conception and development of core components of a novel exploitability engine, including evidence ingestion, entity resolution, and graph construction.
• Ensuring the correctness and reliability of the components shipped, including tests, evaluation, observability, and metrics.
• Collaborating with product, security R&D, and SecOps to understand the problem behind the ticket and turning ambiguous requirements into working code.

• Solid software engineering fundamentals and experience operating production-quality software
• Interest in security problems—vulnerability management, identity security, threat detection, or risk prioritization
• Strong Python, and/or Java, Go, or TypeScript
• Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus
• Working knowledge of distributed systems, APIs, cloud-native development, and data or graph systems
• Hands-on experience building ML- or LLM-powered applications—RAG, embeddings, agents, or probabilistic or ML-driven scoring
• Experience with AI evaluation, safety, or guardrails is a plus
• Experience taking a prototype to a reliable, maintainable production solution
• Strong programming experience in Python and/or Java, Go, or a similar language
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience
• 3+ years of software engineering experience, or equivalent practical experience
• Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability
• Experience building or integrating AI/ML-powered applications in a production or near-production environment
• Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures
• Experience designing and delivering production software systems
• Cybersecurity or security-product experience is a plus
• The primary skills required are Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, and MLOps, which align with advanced machine learning and AI engineering rather than traditional software development