Senior HPCLM Optimization Engineer

Job Title

Senior HPCLM Optimization Engineer

Role Summary

The HPCLM Optimization Engineer analyzes scheduler, workload, HPC, GPU and license telemetry to increase throughput, reduce contention, and improve reliability and user experience for EDA and HPC compute platforms. The role partners with engineering enablement, IT, cloud, and workflow owners to implement tuning, automation, and operational improvements.

Experience Level

Senior-level. This role is advertised as Senior (experienced engineer / Staff Analyst level) and expects several years of relevant HPC/infrastructure experience.

Responsibilities

Primary responsibilities focus on profiling, tuning, and operationalizing optimizations across job scheduling, compute, and license-constrained environments.

• 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 EDA and HPC workloads to assess CPU utilization, GPU acceleration, memory and I/O behavior, runtime performance, and scaling efficiency; translate findings into actionable tuning recommendations.
• Partner with Engineering Enablement, IT, Cloud, workflow owners, and developers to resolve systemic issues and prevent repeat incidents.
• Develop dashboards, KPIs, and reporting for queue health, compute/license utilization, capacity forecasting, and performance/cost optimization outcomes.
• Apply automation and AI/ML-driven approaches for anomaly detection, demand forecasting, and proactive congestion avoidance where appropriate.
• Document best practices, operational playbooks, and repeatable methods to improve resilience and reduce reliance on a small set of experts.

Requirements

Must-have technical skills and experience for effective execution:

• Strong experience in HPC environments and managing enterprise batch schedulers (LSF, Slurm, PBS, or comparable systems).
• Experience with EDA compute workflows, GPU-enabled infrastructure, and large-scale infrastructure operations.
• Hands-on knowledge of Linux systems, scripting/automation, telemetry analysis, and troubleshooting distributed compute environments.
• Experience with workload profiling, performance analysis, and resource optimization for compute- and license-constrained environments.
• Experience driving GPU porting/enablement and 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 verbal and written communication skills for collaboration across engineering, support, and infrastructure teams.

Nice-to-have:

• Experience with HyperScheduler, Slurm, LSF, or comparable enterprise job schedulers.
• Familiarity with FlexLM/license management and EDA-specific workflow tuning.
• Exposure to AWS or hybrid cloud scaling models, capacity forecasting, and cost optimization.
• Experience building dashboards or analytics pipelines using Python, SQL, Grafana, or similar tools.
• Working knowledge of AI/ML techniques for anomaly detection, prediction, or optimization; hands-on exposure to LLMs and AI-assisted automation is a plus.

Education Requirements

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

About the Company

Company: Analog Devices

Headquarters: Norwood, Massachusetts, USA

Analog Devices is a leading global semiconductor company that bridges the physical and digital worlds, enabling breakthroughs at the Intelligent Edge. With a focus on innovation, ADI develops solutions that drive advancements in digitized factories, mobility, and digital healthcare. The company employs around 24,000 people globally and reported revenues exceeding $9 billion in FY24, creating technologies that transform lives across various sectors.