R&D Engineering, Principal 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 10+ years keeping compute infrastructure running for engineers who need it to just work, not sort of work, actually work when they are pushing a deadline or running a critical simulation at 2 a.m. You know that the difference between a good HPC environment and one that slows down a product launch is usually a decision about job scheduling, storage latency, or cloud bursting made six weeks earlier, and you are the kind of engineer who sees that coming and fixes it before anyone files a ticket. You are comfortable moving between on-premises data centers and cloud environments without losing sight of what matters: uptime, performance, and cost. You speak Linux and Python fluently. You have managed LSF, UGE, or Slurm in production, and you know what breaks when a queue gets misconfigured or a node goes rogue. You do not need a perfect requirements doc to get started. You talk to the R&D team, the IT team, the security team, and you figure out what the actual problem is and what the roadmap should look like. At Synopsys, you will work with engineering teams building the tools that design the chips that power everything. The compute environments you manage are not back-office infrastructure. They are the platform that makes the work possible. 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 practices, tooling, and workflows Ability to transl