Principal Machine Learning Engineer (Prisma AIRS)

Palo Alto Networks•Office - USA - CA - Headquarters, CA

6d•$163,200 - $264,000•Hybrid

About The Position

Palo Alto Networks is pioneering the world’s most comprehensive AI security platform through Prisma AIRS—protecting complex ecosystems of AI models, applications, and agents from development through runtime. We are building a brand-new initiative within the AIRS team focused on advancing state-of-the-art AI/ML infrastructure and high-performance, distributed backend systems. We are recruiting senior technical leaders to drive this architecture from the ground up. As a senior technical leader on the AIRS team, you will operate at the intersection of machine learning, cloud security, and AI safety. You will design, build, and deploy high-performance infrastructure and autonomous ML solutions that actively classify threats, tune models, and prevent adversarial attacks at enterprise scale.

Requirements

MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field (or equivalent practical experience) with 8+ years of software engineering industry experience, including a minimum of 3 years dedicated to machine learning, NLP, or AI systems.
Deep programming expertise in Python and strong hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
Proven experience working directly with LLMs/SLMs, advanced prompt engineering, and model fine-tuning techniques.
Strong, demonstrated background in building and scaling auto-classification and anomaly detection models on massive, real-world datasets.

Nice To Haves

Demonstrated knowledge of cybersecurity concepts and AI safety vulnerabilities (e.g., prompt injection defense, AI red-teaming, model security).
Familiarity with DistilBERT and other open-source classification models.
Familiarity with large, graph-based datasets and processing techniques.
Experience with distributed cloud systems (GCP or AWS) and deploying scalable ML inference pipelines at scale.

Responsibilities

Develop scalable anomaly detection pipelines to monitor cloud environments and AI agent actions, ensuring high efficacy with low false-positive and false-negative rates.
Lead SLM (Small Language Model) tuning initiatives, optimizing and fine-tuning models for low-latency, highly accurate edge-cloud security tasks.
Drive advanced data classification strategies, applying machine learning, NLP, and deep learning methods to massive structured and unstructured datasets to extract complex threat patterns.
Be a technical leader who partners closely with product, security, and cloud engineering teams to integrate these ML solutions seamlessly into production systems.