The Role
Optimize and support large-scale HPC job scheduling infrastructure for EDA and GPU-accelerated AI workloads. Improve scheduler performance, utilization, throughput, and reliability across multi-site Linux environments. Develop Python and Bash automation, CI/CD and IaC tooling, LLM-based solutions, observability systems, and containerized workflows. Perform root-cause analysis, resolve infrastructure issues, maintain network licensing, and partner with customer teams to deliver technical improvements.
Summary Generated by Built In
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
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
Skills Required
- Bachelor's degree in Computer Science or a related field, or equivalent experience
- 5+ years of experience operating and supporting large-scale Linux-based compute infrastructure
- Experience developing, fine-tuning, and deploying advanced LLM-based solutions
- Hands-on experience supporting and tuning LSF, Slurm, or similar job scheduling systems in HPC or silicon design environments
- Proficiency in Linux systems administration, including CentOS or RHEL
- Proficiency in CI/CD, GitOps, and Infrastructure as Code
- Proficiency in Python programming and Bash scripting
- Knowledge of FLEXlm network license maintenance, provisioning, automation, monitoring, and troubleshooting
- Experience implementing reliability engineering practices in HPC scheduling environments
- Experience building observability systems with metrics, monitoring, alerting, and performance dashboards
- Experience with Docker, Singularity, or Podman in HPC environments
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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The Company
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.”
