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# Principal Machine Learning Engineer

- **Company:** Palo Alto Networks
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $163,200.0 - $264,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Microsoft Azure, Software as a Service, Cloud Engineering, Software Quality, Continuous Delivery, Continuous Integration, Distributed Systems, Python (Programming Language), Network Security, Machine Learning, Open Source Technology, Tensorflow, Software Deployment, Software Engineering, Google Cloud, Pytorch, Backend, Cloudformation, Containerization, Infrastructure Automation Frameworks, Real-time Inference, Information Technology, Apache Flink, Apache Kafka, Machine Learning Operations, Video Streaming, Software Coding, Terraform, Stream Processing, Software Version Control, Docker, Programming Languages
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_5fb1b1ee5e7cf5c9e177613b5eb0b697

## About the Role

* Strong background on machine learning and ML frameworks (e.g., TensorFlow, PyTorch)
 * Experience with Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation.
 * Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
 * 10+ years of software development experience, with a focus on cloud-native and SaaS applications.
 * Proven experience in designing and building large-scale, distributed systems on public cloud platforms (AWS, GCP, Azure).
 * Strong proficiency in at least one modern programming language such as Python, Go, or Java.
 * Demonstrated experience with the full machine learning lifecycle, including model deployment and MLOps., * 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 technologies, particularly Docker and Kubernetes.
 * Experience with real-time data processing and streaming technologies (e.g., Kafka, Flink).
 * Contributions to open-source projects in the cloud-native or MLOps space.

## Description

Engineering - Our engineering team is at the core of our products and connected directly to the mission of preventing cyberattacks. We are constantly innovating - challenging the way we, and the industry, think about cybersecurity. Our engineers don't shy away from building products to solve problems no one has pursued before. We define the industry instead of waiting for directions. We need individuals who feel comfortable in ambiguity, excited by the prospect of a challenge, and empowered by the unknown risks facing our everyday lives that are only enabled by a secure digital environment., As a Principal Software Engineer, you will provide technical leadership in designing and delivering robust, next-generation cloud security solutions. You will drive the development of scalable cloud security architecture through hands-on coding, manage the full product lifecycle, and collaborate across teams to simplify complex technical issues and deliver high-quality security-as-a-service offerings., * Provide technical leadership for end-to-end solution delivery, collaborating with cross-functional teams (Product, SRE, QA, and Support) to align engineering efforts with business objectives.

 * Drive the development of scalable cloud security architecture through a balance of strategic planning and hands-on coding.
 * Establish and evangelize best practices for model versioning, reproducibility, auditing, and compliance to ensure code quality and data privacy across the organization.
 * Architect and lead the entire ML lifecycle, from initial development and training to production deployment and real-time inference.
 * Build and maintain automated, resilient systems for continuous integration, delivery (CI/CD), and monitoring of backend and machine learning components.
 * Continuously evaluate and integrate cutting-edge MLOps tools and frameworks to enhance system scalability, reliability, and efficiency.
 * Security, Infrastructure & Optimization
 * Design and implement robust, next-generation cloud security solutions to resolve complex backend infrastructure and ML model challenges.
 * Strategically manage and optimize ML infrastructure and pipelines to improve performance, ensure smooth production integration, and reduce operational costs.