Senior AI-ML Data Scientist; ADEM

Requirements

• This role is ideal for a hands-on technical leader who thrives on building advanced reasoning systems, optimizing large-scale data foundations, and mentoring engineering teams to deliver planet-scale impact
• 5+ years in software engineering, with a track record of deploying production-grade ML, GenAI, or agentic workflows at scale
• Strong Python (for AI/ML) combined with professional Go or Java experience for backend microservices
• Hands-on experience with SQL, designing and restructuring Big Query schemas, and building/managing scalable data pipelines using Dataflow, Google ADK and GKE
• Solid understanding of retrieval systems (RAG), prompt engineering, model evaluation, and statistical modeling
• Ability to build platform-level services that integrate cleanly with adjacent ecosystems (e.g., Prisma Access, Cortex)
• BS/MS in Computer Science, Data Science, Engineering, or a related field
• Knowledge of L3-L7 networking is desirable

• Palo Alto Networks' ADEM group is seeking an accomplished Sr. Staff AI-ML / Data Scientist to serve as a principal technical anchor for our next-generation AIOps platform
• In this role, you will bridge the gap between advanced AI research and production-grade backend software engineering, turning massive system telemetry into autonomous, self-healing capabilities
• You will lead the technical design of intelligent, agentic workflows while simultaneously playing a pivotal role in our core backend re-architecture
• Lead the design and implementation of ML/LLM/GenAI-powered agents capable of planning, multi-step execution, self-correction, and seamless collaboration with humans or other agents
• Build advanced reasoning pipelines, retrieval systems (RAG), and knowledge graph integrations that support autonomous task execution and network troubleshooting
• Build, fine-tune, or adapt LLMs, multimodal models, and specialized agent models. Successfully integrate traditional AI/ML and statistical models into these agentic workflows to handle complex endpoint telemetry
• Drive end-to-end solution development from prototype to production, ensuring maximum scalability, reliability, safety, observability, and performance
• Establish best practices for prompt design, agent memory management, tool usage, and context optimization within the agent ecosystem
• Continuously evaluate emerging agent frameworks, LLM tooling, and generative AI technologies to inform platform architecture and roadmap decisions