Sr Principal Machine Learning Engineer; Prisma AIRS

Position: Sr Principal Machine Learning Engineer (Prisma AIRS)
Our Mission

At Palo Alto Networks , we're united by a shared mission-to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you're ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you're in the right place.

Who We Are

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values:
Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!

We believe collaboration thrives in person. That's why most of our teams work from the office full time, with flexibility when it's needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.

Job Summary

Your Career
With Prisma AIRS, Palo Alto Networks is building the world's most comprehensive AI security platform. Organizations are increasingly building complex ecosystems of AI models, applications, and agents, creating dynamic new attack surfaces with risks that traditional security approaches cannot address. In response,Prisma AIRS delivers model security, posture management, AI red teaming, and runtime protection. Our customers can confidently deploy AI-driven innovation while ensuring a formidable security posture from development through runtime.
As a Senior Principal Machine Learning Engineer, you will drive research on cutting-edge areas, including AI-Native Security (LLM, AI Agent, Model Supply-Chain, Runtime AI) and the broader LLM ecosystem security. You will leverage this research to identify and bring up new product opportunities. You will collaborate closely with engineering teams to deploy models, ensuring maximum product impact. Furthermore, you will foster cross-functional collaboration and serve as an AI thought leader both within the company and in the security/LLM community.

Beyond individual contribution, you will lead complex technical projects, mentor senior engineers, and set the standard for performance, scalability, and engineering excellence across the organization. Your decisions will have a profound and lasting impact on our ability to deliver cutting-edge AI security solutions at a massive scale.
Your Impact
Lead the architectural design of a highly scalable, low-latency, and resilient ML inference platform capable of serving a diverse range of models for real-time security applications.

Define technical approaches to less-defined product requirements, ensuring the best fit between product features and technical implementation. Explore new product opportunities by maintaining a deep understanding of LLM and Generative AI research trends.

Technical Leadership:
Provide technical leadership and mentorship to the team, driving best practices in MLOps, software engineering, and system design.

Strategic Optimization:
Drive the strategy for model and system performance, guiding research and implementation of advanced optimization techniques like custom kernels, hardware acceleration, and novel serving frameworks.

Set The Standard:
Establish and enforce engineering standards for automated model deployment, robust monitoring, and operational excellence for all production ML systems.

Cross-Functional Vision:
Act as a key technical liaison to other principal engineers, architects, and product leaders to shape the future of the Prisma AIRS platform and ensure end-to-end system cohesion.

Solve the Hardest Problems:
Tackle the most ambiguous and challenging technical problems in large-scale inference, from mitigating novel security threats to achieving unprecedented performance goals.

Qualifications
Your Experience
BS/MS or Ph.D. in Computer Science, a related technical field, or equivalent practical experience.

Extensive professional experience in software engineering with a deep focus on MLOps, ML systems, or product ionizing machine learning models at scale.

Expert-level programming skills in Python are required; experience in a systems language like Go, Java, or C++ is nice to have.

Deep, hands-on experience designing and building large-scale distributed systems on a major cloud platform (Google Cloud Platform, AWS, Azure, or OCI).

Proven track record of leading the architecture of complex ML systems and MLOps pipelines using technologies like Kubernetes and Docker.

Mastery of ML frameworks (Tensor Flow, PyTorch) and extensive experience with advanced inference optimization tools (ONNX, Tensor

RT).

A strong understanding of popular model architectures (e.g., Transformers, CNNs, GNNs) is a must. A deeper…