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You will combine deep scheduler expertise (LSF, Slurm) with automation, CI/CD, and LLM-based tooling to keep the EDA and GPU-accelerated AI compute environment resilient and measurable.\nWhat you’ll be doing:\nManage, scale, and optimize job scheduling systems (LSF, Slurm, etc.) in a large-scale, multi-site environment supporting EDA and other compute-intensive workloads\nAnalyze scheduler and infrastructure performance data to identify systemic bottlenecks and drive measurable improvements in utilization, throughput, and turnaround time\nBuilding and improving our ecosystem around GPU-accelerated computing\nDefine and develop automation scripts and wrappers tools for IC Design environment\nIdentify recurring operational challenges and implement targeted automation or process improvements to reduce manual effort and prevent repeat incidents\nRoot cause analysis and suggest corrective action for problems large and small scales\nPartner directly with customer teams to clarify requirements, translate technical tradeoffs, and drive issues to closure\nWhat we need to see:\nBachelor’s degree in Computer Science or related field, or equivalent experience\nMinimum 5+ years of experience operating and supporting large-scale Linux-based compute infrastructure\nDevelop, fine-tune, and deploy advanced LLM-based solutions for [specific applications, e.g., NLP, chatbots, content generation, or data analysis\nStrong hands-on experience supporting and tuning job scheduling systems (LSF, Slurm, etc.) in HPC or silicon design environments\nProficiency in Linux systems administration (CentOS/RHEL)\nProficient in modern CI/CD techniques, GitOps and Infrastructure as Code(IaC)\nProficient in Python programming and bash scripting\nWays to stand out from the crowd:\nDeep knowledge of job scheduling systems (LSF, Slurm, etc.) configuration tuning, scheduler internals, and advanced troubleshooting techniques\nNetwork licenses (e.g. FLEXlm) continuous maintenance, provisioning, automation, monitoring and troubleshooting\nExperience implementing reliability engineering practices within HPC scheduling environments\nExperience building or enhancing observability systems, including metrics collection, monitoring pipelines, alerting strategies, and performance dashboards\nBackground with container technologies such as Docker, Singularity, or Podman in HPC environments","description_format":"text","description_chars":2552,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Servers & Data Center Hardware","Computer Components","AI Chips & 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