Sr Staff Software Engineer

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.

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.

The Team Information Technology - Working at a high-tech cybersecurity company within Information Technology is a once-in-a-lifetime opportunity.

As an AI-Native Cloud FinOps Engineer , you will build intelligent pipelines and autonomous optimization systems that control our cloud spend across AWS, Azure, and GCP. Shifting from passive financial monitoring to AI-driven, self-optimizing architectures, you will focus heavily on AI/ML compute infrastructure like GPUs and LLM pipelines. You will bridge the gap between deep systems engineering, machine learning, and multi-cloud financial operations to detect waste. Treating cloud efficiency as a continuous software feature, you will leverage AI to convert potential optimizations into automated savings.

Engineer autonomous systems using LangChain, ADK to inspect, govern, and optimize high-cost AI/ML workloads. Programmatically manage GPU cluster provisioning, optimize model batching, and track per-token unit economics. Write closed-loop software systems that automatically convert detected optimization opportunities into executed savings across clouds. Build infrastructure-as-code pipelines using Terraform and Pulumi embedded with real-time cost-compilation checks. Train and deploy predictive machine learning models to analyze real-time usage telemetry and detect spend anomalies. Lead the technical evolution of our FinOps maturity curve by building telemetry platforms to measure unit economics. Design internal AI agents that continuously analyze infrastructure state, detect waste, and submit pre-validated Pull Requests.

Required Qualifications 8+ years of experience in Software Engineering, Cloud Engineering, SRE, or Cloud FinOps teams. 2+ years of hands-on experience building software tools or AI/ML integrations for cloud resource management. Strong software engineering fundamentals in Python, ReAct, and TypeScript/Node.js. Practical experience interfacing with LLM APIs, vector databases, and agentic frameworks. Deep understanding of the architectural and financial cost drivers of AI/ML workloads. Hands-on programmatic experience interacting with FinOps APIs across AWS, GCP, and Azure. Mastery of IaC tools like Terraform and Pulumi, combined with CI/CD engine experience. Proven track record of architecting and maintaining closed-loop automation tools in multi-cloud environments. Must be local to Bay area to be onsite.

Experience with Kubernetes cost allocation using tools like Kubecost or OpenCost. Proficiency with big-data querying tools like SQL, BigQuery, or Snowflake to process billing exports. Ability to partner with AI/ML Engineers, Product Leaders, and FP&A to align infrastructure