Senior Staff Machine Learning Engineer

Join ServiceNow's Security and Risk Engineering organization as a Senior Staff Machine Learning Engineer. In this role, you will own the architecture of a novel exploitability engine, set technical direction, and mentor senior engineers. You will drive technical direction across architecture, design, and code reviews, and partner with product, security R&D, and SecOps to turn customer problems into architecture. This is a zero-to-one incubation building a new class of exposure analysis that ranks security work by exploitability rather than raw severity.

- Posséder l'architecture de bout en bout d'un moteur d'exploitabilité, de l'ingestion des preuves à la boucle de validation. - Prendre des décisions qui ont des répercussions sur le système, y compris la probabilité calibrée et le classement ordinal. - Diriger le travail de zéro à un à l'échelle de la production, en transformant un problème ambigu et nouveau en un système fiable.

- The judgment to make consequential architecture decisions under uncertainty, and make them defensible to engineers and executives alike - Experience with AI evaluation, safety, governance, or policy guardrails is a plus - Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures - Technical leadership and mentorship that moves teams through influence - Applied interest in security problems—attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response—is strongly preferred - Command of distributed systems, APIs, cloud-native development, and data or graph systems - Strong programming experience in Python and/or Java, Go, or a similar language - 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 - Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and 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 - Hands-on depth in both agentic and LLM systems and probabilistic or ML-driven scoring—graph modeling, calibration, search and optimization, or risk and probability engineering - Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus - 10+ years of software engineering experience, including leading the design and delivery of complex production systems - Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred - A track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software - Expert-level Python, and/or Java, Go, or TypeScript - Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus - Proven zero-to-one at scale: you’ve taken an ambiguous problem to a reliable production system that others depend on - Demonstrated experience as the technical owner or lead for a major system or across teams