Senior HPC DevOps Engineer

We Are Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. You Are You have spent 8+ years building and operating compute infrastructure that powers engineering teams doing work that cannot afford downtime or performance degradation. The systems you run are not back-office IT, they are the platform that determines whether a critical simulation finishes on time or a product milestone slips by three weeks. You understand that reliability at scale is a function of architecture, automation, and the decisions you make about job scheduling, storage, and cloud integration long before anyone notices a problem. You are fluent in Linux, Python, and the operational realities of managing LSF, UGE, or Slurm across large HPC environments. You have debugged misconfigured queues, optimized cloud bursting strategies to balance cost and performance, and worked across IT, security, and R&D to align infrastructure roadmaps with business priorities. You do not need perfect requirements to move forward. You ask the right questions, synthesize conflicting stakeholder needs, and build solutions that work in production, not just in theory. At Synopsys, you will manage the compute environments that enable the design of the chips powering AI, automotive, and next-generation systems. The infrastructure you build directly impacts product timelines and engineering productivity across the company. What You'll Be Doing Enable engineering teams across Synopsys IPG to use on-premises and cloud compute environments for design, verification, and simulation workloads Translate requirements from business, engineering, cybersecurity, and data governance stakeholders into a technical roadmap and execute on it Manage and optimize job scheduler environments including LSF, UGE, or Slurm across large-scale HPC systems Support and troubleshoot Linux/UNIX systems in production, ensuring uptime and performance for engineering workflows Build and maintain DevOps automation using Python and other scripting languages to improve reliability and reduce manual toil Coordinate with IT, R&D, and infrastructure teams to align compute strategy, resolve issues, and deliver on engineering needs Manage cloud compute operations across GCP, AWS, or Azure, including cost optimization, security compliance, and hybrid cloud integration The Impact You Will Have Engineering teams will have reliable, performant compute environments that let them focus on their work instead of fighting infrastructure The roadmap you build will shape how Synopsys scales compute capacity for next-generation IP and EDA development Your automation and scripting will reduce manual operations overhead and improve system reliability across the HPC environment R&D teams will be able to burst to cloud when needed without waiting weeks for approvals or configurations Your coordination across IT, security, and engineering will prevent bottlenecks and misaligned priorities before they slow down product timelines The job scheduler optimizations you implement will improve throughput and reduce queue wait times for critical engineering runs Your deep technical knowledge will make you the go-to resource when something breaks and needs to be fixed fast What You'll Need Deep hands-on experience with Linux/UNIX system administration in production environments Proven experience managing and optimizing job schedulers such as LSF, UGE, or Slurm in large-scale HPC or engineering compute environments Strong scripting and automation skills, particularly in Python Experience supporting engineering teams using cloud platforms like GCP, AWS, or Azure Background in DevOps