Principal ML Architect - Security AI & Advanced Model Systems

About Us: Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people. How We Work: At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values: Bold in how we dream and innovate Responsive to feedback, challenges and opportunities Accountable for results and best in class outcomes Visionary in future focused problem-solving Exceptional in execution and impact Role Overview We are seeking a Principal ML Architect to lead the design and development of next-generation AI systems for cybersecurity , leveraging state-of-the-art LLMs/SLMs and advanced machine learning techniques . This role requires deep expertise in model architecture, training, fine‑tuning, and distillation , combined with a strong understanding of security domains such as threat detection, anomaly detection, data protection, and AI safety . You will drive the development of intelligent, security-focused AI systems and agents capable of operating at scale across high-volume, adversarial, and sensitive environments , while ensuring robustness, explainability, and compliance . AI Architecture for Security Systems Design and lead architecture for AI-driven security platforms , including: Threat detection and behavioral analytics Data loss prevention (DLP) and insider risk detection AI usage monitoring and policy enforcement (GenAI security) Build systems that process high-volume, high-velocity security telemetry in real time Model Development & Innovation Lead development of state-of-the-art SLMs/LLMs tailored for security use cases: Log analysis, alert triage, threat intelligence, policy reasoning Drive experimentation with modern architectures (Transformers, MoE, retrieval‑augmented systems, hybrid models) Balance trade‑offs between model accuracy, latency, interpretability, and cost Training, Fine‑Tuning & Distillation Architect pipelines for: Domain adaptation and instruction tuning on security‑specific datasets Model distillation and compression for efficient deployment in enterprise environments Design and execute experiments for: Alignment (RLHF/RLAIF) in security‑sensitive contexts Red‑teaming and adversarial robustness of models AI Agents for Security Workflows Design and oversee AI agents that: Automate security operations (SOC workflows, triage, investigation) Integrate with enterprise tools (SIEM, EDR, SaaS platforms) Define architectures for tool use, reasoning, memory, and policy‑aware decision making Experimentation & Evaluation Establish rigorous evaluation frameworks for: Detection accuracy, false positives/negatives Model robustness under adversarial conditions Safety, hallucination, and misuse risks Lead deep experimentation cycles to continuously improve model performance and reliability Productionization & Scale Guide deployment of models into enterprise‑scale, real‑time environments Optimize inference systems for low latency, high throughput, and cost efficiency Collaborate with platform teams on ML infrastructure, data pipelines, and observability Security, Governance & Responsible AI Ensure models and systems meet enterprise security standards (SOC2, ISO, GDPR, etc.) Establish best practices for: Secure model development and deployment Data privacy and protection in training pipelines Responsible AI and model safety in adversarial environments Required Qualifications Experience 10+ years in ML/AI systems, with significant focus on deep learning and production ML Proven experience in: Building or scaling LLMs/SLMs or advanced ML systems Applying ML/AI in security, fraud, risk, or adversarial domains Track record of delivering production‑grade AI systems at scale Technical Expertise Deep understanding of: Transformer architectures and modern LLM techniques Retrieval‑augmented generation (RAG) and hybrid AI systems Model training dynamics, scaling laws, and optimization Hands‑on experience with: Training, fine‑tuning, and distilling models Efficient inference (quantization, pruning, batching) Distributed training frameworks (PyTorch, DeepSpeed, FSDP, etc.) Security Domain Knowledge Strong understanding of one or more: Security telemetry (logs, network traffic, endpoint data) Threat detection and anomaly detection systems Identity, access, and data protection systems Familiarity with security tooling ecosystems (SIEM, EDR, CASB, etc.) Systems & Engineering Experience designing high‑throughput, low‑latency ML systems Strong programming skills in Python , with production experience Understanding of data pipelines, feature engineering, and MLOps practices Preferred Qualifications Experience building AI systems for SaaS security or GenAI security platforms Familiarity with multi‑agent systems for security automation Experience with synthetic data generation for security use cases Contributions to AI/ML research, open‑source, or security tooling Background in AI safety, adversarial ML, or model interpretability Success Metrics Development of high‑precision, low‑noise security AI models Successful deployment of AI agents automating security workflows Measurable improvements in detection accuracy and operational efficiency Robustness of models against adversarial and real‑world attack scenarios Strong adherence to enterprise security, privacy, and compliance standards Why This Role Matters This role is central to building intelligent, AI‑native security systems that can operate at enterprise scale and under adversarial conditions . The Principal ML Architect will define the technical foundation for secure, reliable, and high‑performance AI systems , enabling the organization to lead in next‑generation cybersecurity powered by AI . Why Proofpoint At Proofpoint, we believe that an exceptional career experience includes a comprehensive compensation and benefits package. Here are just a few reasons you’ll love working with us: Competitive compensation Comprehensive benefits Career success on your terms Flexible work environment Annual wellness and community outreach days Recognition for your contributions Global collaboration and networking opportunities Base Pay Ranges SF Bay Area, New York City Metro Area: Base Pay Range: 254,000.00 - 349,250.00 USD California (excludes SF Bay Area), Colorado, Connecticut, Illinois, Washington DC Metro, Maryland, Massachusetts, New Jersey, Texas, Washington, Virginia, and Alaska: Base Pay Range: 208,800.00 - 287,100.00 USD All other cities and states excluding those listed above: Base Pay Range: 187,000.00 - 257,180.00 USD EEO Statement Proofpoint is an equal opportunity employer; we hire without consideration to race, religion, creed, color, national origin, age, gender, sexual orientation, marital status, veteran status or disability. How to Apply Interested? Submit your application along with any supporting information—we can’t wait to hear from you! #J-18808-Ljbffr Proofpoint