Principal Machine Learning Platform Engineer (Prisma AIRS)

About the role

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.

Responsibilities

• 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.
• Provide technical leadership and mentorship to the team, driving best practices in MLOps, software engineering, and system design.
• 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.
• Establish and enforce engineering standards for automated model deployment, robust monitoring, and operational excellence for all production ML systems.
• 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.
• Tackle the most ambiguous and challenging technical problems in large-scale inference, from mitigating novel security threats to achieving unprecedented performance goals.

Requirements

• 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 productionizing 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 (GCP, 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 (TensorFlow, PyTorch) and extensive experience with advanced inference optimization tools (ONNX, TensorRT).
• A strong understanding of popular model architectures (e.g., Transformers, CNNs, GNNs) is a significant plus.
• Demonstrated expertise with modern LLM inference engines (e.g., vLLM, SGLang, TensorRT-LLM) is required.
• Open-source contributions in these areas are a significant plus.
• Experience with low-level performance optimization, such as custom CUDA kernel development or using Triton Language, is a plus.
• Experience with data infrastructure technologies (e.g., Kafka, Spark, Flink) is great to have.
• Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, Tekton) is a plus.

Qualifications

• 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 productionizing 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 (GCP, 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 (TensorFlow, PyTorch) and extensive experience with advanced inference optimization tools (ONNX, TensorRT).
• A strong understanding of popular model architectures (e.g., Transformers, CNNs, GNNs) is a significant plus.
• Demonstrated expertise with modern LLM inference engines (e.g., vLLM, SGLang, TensorRT-LLM) is required.
• Open-source contributions in these areas are a significant plus.
• Experience with low-level performance optimization, such as custom CUDA kernel development or using Triton Language, is a plus.
• Experience with data infrastructure technologies (e.g., Kafka, Spark, Flink) is great to have.
• Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, Tekton) is a plus.

Skills

• Expert-level programming skills in Python.
• Deep, hands-on experience designing and building large-scale distributed systems on a major cloud platform (GCP, AWS, Azure, or OCI).
• Mastery of ML frameworks (TensorFlow, PyTorch).
• Advanced inference optimization tools (ONNX, TensorRT).
• Modern LLM inference engines (e.g., vLLM, SGLang, TensorRT-LLM).
• Low-level performance optimization, such as custom CUDA kernel development or using Triton Language.
• Data infrastructure technologies (e.g., Kafka, Spark, Flink).
• CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, Tekton).

Benefits

We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.

Pay

$157,200.00 - $254,100.00/yr

Schedule

This role is based full-time in our Palo Alto, CA office.

Our Commitment

We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.

Is role eligible for Immigration Sponsorship?

Yes