Principal ML Engineer — AI Security Runtime (Hybrid)

Palo Alto Networks, Inc.; California; Hybrid; USD 163,000 - 264,000; Full time

Job summary

Palo Alto Networks, Inc. in Santa Clara, CA is seeking a senior technical leader for the AIRS team to design, build, and deploy high‑performance ML infrastructure at enterprise scale.

You will work at the intersection of machine learning, cloud security, and AI safety, guiding architecture from ground up and partnering with product, security, and cloud engineering teams. Compensation includes base salary plus restricted stock units and a bonus, with a hybrid work model (3 days on-site at

Qualifications

• MS or PhD in Computer Science, AI, ML, Statistics or related field with 8+ years software eng. experience; 3+ years in ML/NLP/AI systems.
• Strong programming in Python and deep learning frameworks like PyTorch or TensorFlow.
• Experience with LLMs/SLMs, prompt engineering, and model fine-tuning.
• Background in auto-classification and anomaly detection on large datasets.

Responsibilities

• Develop scalable anomaly detection pipelines for cloud and AI agent actions.
• Lead SLM tuning for low-latency edge-cloud tasks.
• Drive data classification using ML/NLP/deep learning to detect complex threat patterns.
• Collaborate with product, security, and cloud teams to deploy ML solutions in production.

Skills

Python

LLMs/SLMs

Prompt engineering

Model fine-tuning

Cloud security

NLP

AI systems

Education

MS or PhD in CS/AI/ML/Stats

Equivalent practical experience

Tools

PyTorch

TensorFlow

GCP

Job description

Palo Alto Networks, Inc. in Santa Clara, CA is seeking a senior technical leader for the AIRS team to design, build, and deploy high‑performance ML infrastructure at enterprise scale.

You will work at the intersection of machine learning, cloud security, and AI safety, guiding architecture from ground up and partnering with product, security, and cloud engineering teams. Compensation includes base salary plus restricted stock units and a bonus, with a hybrid work model (3 days on-site at