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. Success in this role requires both operational precision along with developing and supporting forward-looking resource management solutions that address evolving compute demands. Beyond day-to-day operations, the role drives improvements in observability, service reliability, and automation, ensuring the EDA compute environment remains resilient, measurable, and aligned with long-term engineering demands.
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
Lead problem solving across scheduler, OS, and workload layers, ensuring timely resolution of service-impacting issues
Identify recurring operational challenges and implement targeted automation or process improvements to reduce manual effort and prevent repeat incidents
Help define and track reliable metrics and SLOs for service performance and reliability, partnering with customers to ensure expectations are realistic and measurable
Contribute to operational standards, documentation, and best practices to improve consistency across sites
Partner directly with customer teams to clarify requirements, translate technical tradeoffs, and drive issues to closure
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
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)
Strong problem solving skills and the ability to independently analyze complex system behavior under load
Clear and effective communication skills, including the ability to articulate technical tradeoffs and reliability metrics to engineering stakeholders
Ways to stand out from the crowd:
Experience implementing reliability engineering practices within HPC scheduling environments
Deep knowledge of job scheduling systems (LSF, Slurm, etc.) configuration tuning, scheduler internals, and advanced troubleshooting techniques
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
Experience influencing adoption of new infrastructure standards across multiple teams or sites
NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most forward-thinking and hardworking people in the world on our team and our collaborative talent continues to drive NVIDIA's growth. We are seeking creative and independent engineers with real passion for technology!
#LI-Hybrid
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
