as a member of the hardware infrastructure eda compute team, you will optimize, scale, and support workload scheduling systems that directly impact design velocity and infrastructure efficiency. 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.
what you'll be doing:
manage, scale, and optimize job scheduling systems (lsf, slurm, etc.) in a large-scale, multi-site environment supporting eda and other compute-intensive workloads
analyze scheduler and infrastructure performance data to identify systemic bottlenecks and drive measurable improvements in utilization, throughput, and turnaround time
building and improving our ecosystem around gpu-accelerated computing
define and develop automation scripts and wrappers tools for ic design environment
identify recurring operational challenges and implement targeted automation or process improvements to reduce manual effort and prevent repeat incidents
root cause analysis and suggest corrective action for problems large and small scales
partner directly with customer teams to clarify requirements, translate technical tradeoffs, and drive issues to closure
Requirements: what we need to see:
bachelor's degree in Computer Science or related field, or equivalent experience
minimum 5+ years of experience operating and supporting large-scale Linux -based compute infrastructure
develop, fine-tune, and deploy advanced llm-based solutions for [specific applications, e.g., NLP, chatbots, content generation, or data analysis
strong hands-on experience supporting and tuning job scheduling systems (lsf, slurm, etc.) in hpc or silicon design environments
proficiency in Linux systems administration (centos/rhel)
proficient in modern ci/cd techniques, gitops and infrastructure as code(iac)
proficient in Python programming and bash scripting
ways to stand out from the crowd:
deep knowledge of job scheduling systems (lsf, slurm, etc.) configuration tuning, scheduler internals, and advanced troubleshooting techniques
network licenses (e.g. flexlm) continuous maintenance, provisioning, automation, monitoring and troubleshooting
experience implementing reliability engineering practices within hpc scheduling environments
experience building or enhancing observability systems, including metrics collection, monitoring pipelines, alerting strategies, and performance dashboards
background with container technologies such as docker, singularity, or podman in hpc environments
This position is open to all candidates.