Principal Machine Learning Engineer

Palo Alto Networks is building next-generation cloud security, and this Principal Machine Learning Engineer role supports that mission through hands-on technical leadership. You’ll own end-to-end work across the machine learning lifecycle, helping design scalable architectures, deploy models for real-time inference, and strengthen reliability with CI/CD, monitoring, and MLOps practices. The team works full time from the office for in-person collaboration, with flexibility when needed.

What you’ll do

• Provide technical leadership to deliver end-to-end solutions by collaborating with cross-functional teams including Product, SRE, QA, and Support.
• Drive the development of scalable cloud security architecture through a balance of strategic planning and coding.
• Architect and lead the full ML lifecycle, from initial development and training to production deployment and real-time inference.
• Build and maintain automated, resilient systems for CI/CD and monitoring across backend and machine learning components.
• Establish best practices for model versioning, reproducibility, auditing, and compliance to support code quality and data privacy.
• Continuously evaluate and integrate cutting-edge MLOps tools and frameworks to improve scalability, reliability, and efficiency.
• Design and implement robust, next-generation cloud security solutions to address complex backend infrastructure and ML model challenges.
• Strategically manage and optimize ML infrastructure and pipelines to improve performance, reduce operational costs, and ensure smooth production integration.

What you bring

• Strong background in machine learning and ML frameworks, such as TensorFlow and PyTorch.
• Experience with Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation.
• 10+ years of software development experience, focused on cloud-native and SaaS applications.
• Proven experience designing and building large-scale, distributed systems on public cloud platforms including AWS, GCP, or Azure.
• Strong proficiency in at least one modern programming language such as Python, Go, or Java.
• Demonstrated end-to-end ML lifecycle experience, including model deployment and MLOps.
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Technologies you’ll work with

• ML and frameworks: TensorFlow, PyTorch
• Cloud and IaC: Terraform, CloudFormation, AWS, GCP, Azure
• Languages: Python, Go, Java
• Delivery and platforms: CI/CD, Docker, Kubernetes
• Data and streaming: Kafka, Flink
• MLOps: MLOps (plus evaluation and integration of tools/frameworks)

Team and workplace

• Engineering is directly connected to the mission of preventing cyberattacks, with a culture of challenging assumptions and defining the industry through innovation.
• Teams collaborate in person, with most teams working from the office full time and flexibility when needed.

Preferred qualifications

• Master’s or PhD in Computer Science or a related technical field.
• Experience in the cybersecurity domain or with network security products.
• Expertise with containerization and orchestration, particularly Docker and Kubernetes.
• Experience with real-time data processing and streaming technologies such as Kafka and Flink.
• Contributions to open-source projects in the cloud-native or MLOps space.

Location: Santa Clara, CA, United States (onsite). Compensation: USD 163,200 - 264,000 per year.