Support the reliability and performance of distributed cloud systems by assisting in monitoring and identifying issues
Participate in troubleshooting production issues and contribute to root cause analysis with guidance from senior engineers
Assist in improving CI/CD pipelines and deployment processes to enhance reliability and efficiency
Build and maintain simple automation and tooling to reduce manual processes
Help monitor system performance, including latency and system behavior, and support troubleshooting efforts
Work with engineering teams to improve observability and monitoring practices
Participate in code reviews and team discussions to learn best practices
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
Strong foundational knowledge of computer science fundamentals (data structures, algorithms)
Basic understanding of:
distributed systems concepts
operating systems
networking (TCP/IP, DNS)
Programming experience in Python, Go, Java, or similar
Internship or project experience with:
cloud platforms (AWS, Azure, or GCP)
containers (Docker) or orchestration (Kubernetes)
CI/CD systems
Ability to learn quickly and work through technical problems with guidance
Nice to Have
Exposure to Infrastructure as Code (Terraform or similar)
Familiarity with monitoring or observability tools
Interest in cloud infrastructure, reliability, and security systems
Why This Role
Gain hands-on experience working on distributed systems at scale
Learn how production systems behave and how to troubleshoot real-world issues
Build a strong foundation in platform engineering and reliability
Grow within a team of experienced engineers in a high-impact environment
AI-First Engineering Environment At Palo Alto Networks, we embrace an AI-first approach to engineering. Engineers are encouraged to leverage AI-powered tools to accelerate learning, improve productivity, and enhance problem-solving. Success in this role is not about knowing every answer, but about effectively combining strong engineering fundamentals, curiosity, sound judgment, and modern AI-assisted workflows. We are interested in candidates who are excited to learn how AI can be applied responsibly across software development, operations, troubleshooting, and platform engineering.