Engineers at Analog Devices (ADI) solve some of the world’s most complex engineering problems. To be able to do so with ease, they need access to the best IC design infrastructure available. We, the Engineering Enablement (EE) team at ADI, aim to do just that, by building the infrastructure and support required by ADI’s design community.   

The HPCLM Optimization Engineer will work across Job Scheduler, EDA Workloads, HPC infrastructure, GPU Accelerators, cloud scale-out patterns, workflow behavior, and license operations to identify and implement improvements that increase throughput, reduce contention, and improve user experience. Depending on candidate profile and leveling, this role may be hired as a Senior Engineer (deep technical execution with ownership of a defined optimization area) or Staff Analyst (broader cross-functional analysis, optimization strategy, and KPI-driven recommendations across the HPCLM landscape). 

Responsibilities and Duties include but are not limited to: 

• Analyze cluster, queue, workload, and license telemetry to identify optimization opportunities across throughput, fairness, efficiency, and reliability. 

• Drive scheduler and policy tuning for job placement, queue design, fairshare behavior, right-sizing, and license-aware execution. 

• Profile critical EDA and HPC workloads to analyze CPU utilization, GPU acceleration characteristics, memory and I/O behavior, runtime performance, and scaling efficiency, and translate findings into actionable tuning and optimization recommendations. 

• Partner with Engineering Enablement, IT, Cloud, workflow owners, and developers to resolve systemic issues and prevent repeat incidents. 

• Develop dashboards, KPIs, and data-driven reporting for queue health, Compute/License utilization, capacity forecasting, and performance and cost optimization outcomes. 

• Apply automation and AI/ML-driven approaches where appropriate for anomaly detection, demand forecasting, and proactive congestion avoidance. 

• Document best practices, operational playbooks, and repeatable methods to improve resilience and reduce reliance on a small set of experts. 

Required Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Electronics, Data Engineering, or a related technical field. 

• Strong experience in HPC environments, batch schedulers (LSF/Slurm/PBS etc), EDA compute workflows, GPU Enabled Infrastruture or large-scale infrastructure operations. 

• Hands-on knowledge of Linux systems, scripting/automation, telemetry analysis, and troubleshooting of distributed compute environments. 

• Experience with workload profiling, performance analysis, or resource optimization for compute and license-constrained environments. 

• Experience driving GPU porting and enablement including GPU-aware scheduler configuration and resource allocation policies. 

• Ability to translate operational data into recommendations, influence cross-functional stakeholders, and drive actions to closure. 

• Strong communication skills with the ability to work effectively across engineering, support, workflow, and infrastructure teams. 

Preferred Qualifications 

• Experience with HyperScheduler, Slurm, LSF, or comparable enterprise job schedulers. 

• Experience with EDA workload behavior, FlexLM/license management, or workflow-specific HPC tuning. 

• Exposure to AWS or hybrid cloud scaling models, capacity forecasting, and cost optimization. 

• Working knowledge of AI/ML techniques for anomaly detection, prediction, or optimization in operational systems. 

• Experience building operational dashboards or analytics pipelines using Python, SQL, Grafana, or related tools. 

• Hands‑on or applied exposure to LLMs and AI‑assisted automation, EDA AI techniques, including intelligent agents and optimization‑driven flows