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ServiceNow's Platform Security Core team is building foundational security infrastructure and AI-driven detection systems for enterprise-scale operations. This Senior Staff AI Security Engineer role is a hands-on technical leadership position focused on architecting and delivering next-generation AI security solutions that integrate machine learning, reasoning engines, and real-time inference into core security systems.
You will design and implement ML-driven security systems for identity risk assessment, anomalous access detection, malware classification, and sensitive data discovery. Key responsibilities include building high-performance inference pipelines and contextual reasoning systems that apply models in real-time across distributed security decisions, integrating AI into access control and identity systems through contextual analysis and adaptive authentication, and developing attack detection and threat classification models with focus on false-positive reduction and operational efficiency.
You'll architect modular, reusable ML systems including ML platforms, feature engineering frameworks, and model management infrastructure that teams can adopt and extend. The role requires designing for observability and model performance monitoring in security-critical production environments, collaborating across identity, access control, threat detection, and infrastructure teams to integrate AI solutions end-to-end, and staying current with advances in AI/ML including transformer models, reasoning engines, and retrieval-augmented generation.
Required qualifications include a Bachelor's degree with 10+ years of software development experience (or Master's with 8+ years, or PhD with 6+ years). You need hands-on experience implementing machine learning algorithms from scratch, deep programming expertise in Java and/or Python with systems-level knowledge, and a proven track record building and deploying ML systems in production at significant scale. Strong fundamentals in computer science, algorithms, data structures, and distributed systems are essential.
You should have deep understanding of ML fundamentals, hands-on experience with neural networks and deep learning frameworks (TensorFlow, PyTorch), experience training and tuning models to production-grade quality, and understanding of model inference optimization. Experience with LLMs, foundation models, fine-tuning, RAG, and agentic systems is required. Deep knowledge of identity and access control systems, experience applying ML to security problems, understanding of sensitive data landscapes and PII detection, and familiarity with security operations at scale round out the technical requirements.
