Senior Staff Machine Learning Engineer, Palo Alto Networks

• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Experience: 5+ years of industry experience in machine learning, data analytics, and software engineering
• Programming: Expertise in Python (or Go)
• LLM Expertise: Proven experience working with large language models (e.g., open ai, LLAMA, GEMINI.)
• Deep Learning: Strong theoretical or empirical understanding of deep learning techniques and frameworks
• ML Model Deployment: Experience in building, testing, and deploying machine learning models, particularly large language models
• Debugging & Analytical Skills: Strong ability to troubleshoot and optimize models
• Cloud & Distributed Computing: Familiarity with building applications using Google cloud platform(GCP) computing environments
• MLOps/LLMOps: Experience with DevOps/MLOps practices for machine learning
• Communication: Excellent communication skills for explaining technical concepts and collaborating with cross-functional teams
• Passion: A keen interest in staying updated with the latest AI and machine learning trends

Desirable

• We are seeking a Sr Staff LLM Application Engineer to join our team and lead the development and optimization of cutting-edge Large Language Models (LLMs) within the cybersecurity domain
• You will play a pivotal role in advancing our AI technology, leveraging Generative AI, LLMs, and ML Ops to drive impactful solutions
• As a technical leader, you will lead high-impact projects, mentor cross-functional teams, and contribute to the strategic direction of our AI-driven cybersecurity initiatives
• Develop & Optimize LLMs: Lead the design, fine-tuning, and optimization of state-of-the-art LLMs for various cybersecurity applications, focusing on both performance and accuracy
• Model Evaluation: Conduct in-depth evaluations of LLMs, assessing their effectiveness, efficiency, and business alignment
• AI Technology Integration: Implement advanced AI technologies, such as Retrieval-Augmented Generation (RAG), function calling, and code interpreters, to enhance LLM capabilities
• Research & Development: Stay at the forefront of advancements in machine learning, particularly in LLMs, LLM agents, and large-scale neural networks
• Parallel Training Techniques: Utilize data and model parallel training techniques to efficiently manage large-scale models
• Cross-Functional Leadership: Work closely with ML engineers, data scientists, and product teams to guide, mentor, and foster collaboration across disciplines
• Documentation & Communication: Maintain detailed documentation of models, methodologies, and findings, ensuring clear communication across the organization
• Product Strategy: Contribute to the AI-driven product roadmap, vision, and strategic direction
• New Initiatives & Architecture: Lead the incubation of new initiatives, design scalable AI/ML solutions, and drive strategic technology choices for delivery within a microservices architecture
• Model Deployment: Design, test, and deploy machine learning models, including LLMs, and develop scalable pipelines for both batch and real-time use cases