Senior Staff AI Security Engineer

Join our team as a Senior Staff AI Security Engineer, where you will architect and deliver enterprise-scale AI security solutions. You will leverage your strong technical leadership and hands-on machine learning expertise to design and operate intelligent security systems. Your responsibilities will include building ML-driven security systems, integrating AI into access control and identity, developing attack detection and threat classification, and leading complex technical initiatives. You will collaborate with teams across security and infrastructure and stay current with advances in AI/ML.

• Architect and deliver enterprise-scale AI security solutions that integrate machine learning, reasoning engines, and real-time inference into core security systems.
• Design and implement ML-driven security systems, build machine learning algorithms for identity risk assessment, anomalous access detection, malware classification, and sensitive data discovery.
• Lead complex technical initiatives, provide technical leadership for multi-quarter efforts that combine ML research, systems engineering, and security domain expertise.

• Familiarity with model observability and monitoring: detecting model drift, performance degradation, and retraining strategies
• Hands-on experience with neural networks, deep learning frameworks (TensorFlow, PyTorch), and modern model architectures
• Understanding of model inference: latency optimization, quantization, model serving infrastructure, and real-time prediction pipelines
• Experience applying machine learning to security problems: anomaly detection, attack classification, risk scoring, and threat pattern recognition
• Understanding of sensitive data landscapes: PII detection, data classification frameworks, and data governance strategies
• Deep programming expertise in Java and/or Python, including systems-level knowledge and performance optimization
• Experience with feature engineering, feature stores, and ML data pipelines at scale
• Experience training, tuning, and deploying models: hyperparameter optimization, regularization, preventing overfitting, and achieving production-grade model quality
• Experience integrating security solutions with platform infrastructure: API design, event streaming, and decision-making in critical paths
• Familiarity with security operations: how detection systems, alert triage, and incident response workflows operate at scale
• Proven track record building and deploying machine learning systems in production environments at significant scale
• Hands-on experience implementing machine learning algorithms from scratch—not just using libraries, but understanding how models work at a fundamental level
• Deep knowledge of identity and access control systems: how authentication, authorization, and access decisions flow through enterprise systems
• Knowledge of common attack patterns and threat models relevant to enterprise security
• Knowledge of reasoning and agentic systems: how to apply contextual analysis, multi-step reasoning, and decision logic on top of models
• Bachelor's degree with 10+ years of software development experience; OR Master's degree with 8+ years; OR PhD with 6+ years; OR equivalent work experience
• Experience with LLMs and large-scale foundation models: fine-tuning, retrieval-augmented generation (RAG), prompt engineering at scale, and understanding of model weights and token economies
• Strong fundamentals in computer science: algorithms, data structures, complexity analysis, system design, and distributed systems
• Deep understanding of machine learning fundamentals: supervised learning, unsupervised learning, model evaluation, feature engineering, and model selection
• Experience with Kafka, stream processing, or real-time data systems for security applications
• Hands-on work with cryptography, zero-trust architectures, or OAuth/mTLS
• Experience deploying models in regulated environments with compliance and governance requirements
• Track record mentoring junior engineers and driving technical excellence across teams
• Open-source contributions to ML or security projects