Principal Machine Learning Engineer (Prisma AIRS)

Palo Alto Networks is building Prisma AIRS, an AI security platform designed to protect AI models, applications, and agents across the lifecycle, including development and runtime. This role supports a brand-new initiative within the AIRS (Artificial Intelligence Runtime Security) team, creating modern AI/ML infrastructure and high-performance distributed backend systems to classify threats and help prevent adversarial attacks at enterprise scale.

Working as a senior technical leader in Santa Clara, CA (hybrid, 3 days/week on-site at Corporate HQ), you will design and deploy autonomous ML solutions that combine anomaly detection pipelines, model tuning, and large-scale data classification. The work sits directly at the intersection of machine learning, cloud security, and AI safety.

What You’ll Do

• Develop scalable anomaly detection pipelines to monitor cloud environments and AI agent actions, targeting high efficacy with low false-positive and false-negative rates.
• Lead SLM (Small Language Model) tuning initiatives, optimizing fine-tuning and model behavior for low-latency, high-accuracy edge-cloud security tasks.
• Drive data classification strategies using machine learning, NLP, and deep learning across massive structured and unstructured datasets to extract complex threat patterns.
• Partner with product, security, and cloud engineering teams to integrate ML solutions into production systems.

What You Bring

• MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field (or equivalent practical experience).
• 8+ years of software engineering industry experience, including 3+ years focused on machine learning, NLP, or AI systems.
• Deep programming expertise in Python.
• Hands-on experience with deep learning frameworks including PyTorch or TensorFlow.
• Proven experience working with LLMs/SLMs, including advanced prompt engineering and model fine-tuning.
• Strong background building and scaling auto-classification and anomaly detection models on large, real-world datasets.
• Knowledge of cybersecurity concepts and AI safety vulnerabilities, such as prompt injection defense, AI red-teaming, and 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.

Tools and Technologies

Python, PyTorch, TensorFlow, LLMs, SLMs, NLP, DistilBERT, GCP, AWS

Level and Team

• Levels considered: Sr. Staff and Principal Engineers
• Team: AIRS (Artificial Intelligence Runtime Security)

Compensation and Sponsorship

Salary range: $163,200.00 - $264,000.00/yr. Compensation depends on qualifications, experience, and work location, and may include restricted stock units and a bonus. Immigration sponsorship: Yes.