Senior Compute Platform Engineer, LSF - EDA Infrastructure

Posted 22 Days Ago
Be an Early Applicant
2 Locations
In-Office or Remote
184K-357K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Own and optimize IBM Spectrum LSF scheduling across 15–25 federated cells. Diagnose scheduler latency, MultiCluster forwarding and cross-cluster contention, design scalable cell topology, and encode scheduler policy through infrastructure as code. Partner with CAD and methodology teams on large-scale EDA workloads, license constraints, and tape-out bursts. Requires deep LSF internals expertise, production MultiCluster experience, strong Linux fundamentals, and Python, Perl, and shell scripting skills.
Summary Generated by Built In

NVIDIA's silicon does not tape out without the farm behind it. Our EDA compute environment runs millions of cores across federated LSF cells, and every simulation, synthesis run, and timing signoff on our roadmap passes through it. We are consolidating a dual-scheduler estate onto a single LSF platform, and we are looking for an engineer who knows LSF at the level of its internals — not just its configuration files.

This is a deep-specialist role. You will be the person the team escalates to when scheduling latency creeps up and nothing in the logs explains why.

What you'll be doing:

  • Owning scheduler behavior across 15–25 federated LSF cells, including mbatchd and mbschd tuning, scheduling cycle analysis, and the contention patterns that appear as cells approach host-count ceilings.

  • Diagnosing MultiCluster forwarding problems — remote queue sizing, forwarding policy, cross-cluster pend behavior — where the symptom reported by users is "the farm is slow" rather than a clear failure.

  • Setting the technical design for cell topology and federation as the farm grows, and deciding what belongs in a cell versus what belongs in a new one.

  • Working with our IaC engineer to encode scheduler policy into a config schema that survives contact with MultiCluster, rather than one that looks clean and breaks at scale.

  • Partnering directly with CAD and methodology teams on workloads that break normal assumptions: 500GB+ memory jobs, interactive-versus-batch contention, and tape-out crunch bursts.

What we need to see:

  • BS or MS in Computer Science, Computer Engineering, or equivalent experience.

  • 8+ years in HPC or large-scale batch compute, with 5+ years of that on IBM Spectrum LSF.

  • Demonstrated depth in LSF internals — you have debugged scheduler behavior beyond what the documentation covers, and you can explain a scheduling cycle from submission to dispatch.

  • Hands-on MultiCluster experience in a production, multi-site environment.

  • Strong Linux systems fundamentals, system programming languages and scripting in Python, Perl, and shell.

Ways to stand out from the crowd:

  • You have worked on LSF as a developer or in escalation engineering, rather than only as a consumer of it.

  • Experience with LSF integration points: esub, eexec, elim, submit wrappers, RTM, or the LSF APIs.

  • Background in semiconductor or EDA compute, where license constraints and job constraints compete.

  • You have migrated a production estate off Slurm, PBS, or Grid Engine without a scheduled outage users noticed.

#LI Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 13, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

Skills Required

  • BS or MS in Computer Science, Computer Engineering, or equivalent experience
  • 8+ years of experience in HPC or large-scale batch compute
  • 5+ years of experience with IBM Spectrum LSF
  • Demonstrated expertise in LSF internals and scheduler behavior
  • Hands-on MultiCluster experience in a production, multi-site environment
  • Strong Linux systems fundamentals
  • Programming and scripting experience in Python, Perl, and shell

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.

  • 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.
  • 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.
  • 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
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

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.”

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