Senior HPC Architect, Automation and At-Scale Deployment

Nvidia

Confirmed live 2 days ago High trust

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $184,000–$287,500 / yr
Posted: 6 days ago
Freshness: Confirmed live 2 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k

This role $236k

$148k most similar roles pay here $302k

This role pays more than 75% of similar roles. Most pay $171,125–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior HPC Architect, Automation and At-Scale Deployment

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As a Senior HPC Architect, Automation and At-Scale Deployment, you will join the team to support the deployment and bringup of large-scale GPU compute clusters. You will provide engineering solutions to operationalize GPU computing products and software stacks while serving as an internal reference for system administration and at-scale system analysis within the technical community. Your daily work involves collaborating with researchers and developers to craft improved workflows, developing differentiated solutions, and implementing tuning mechanisms for large-scale compute runs. The role requires proficiency in C, C++, Python, and Bash scripting, alongside experience with parallel filesystems, Linux performance tools, and container technology. You will solve complex problems related to accelerated computing scheduling and I/O stacks to enable advanced breakthroughs in artificial intelligence and high-performance computing across various hardware and software platforms.

Skills

GPU HPC Deep Learning C++ Python Bash Linux Parallel Filesystems Accelerated Computing System Administration Telemetry Visualization Pipelines I/O Stacks

What you'll do

Support the deployment and bringup of large-scale GPU compute clusters.
Implement at-scale system administration and tuning mechanisms for large-scale compute runs.
Develop engineering solutions to operationalize latest GPU computing products and software stacks.
Architect, develop, and bring up large-scale performance platforms with internal and external partners.
Create improved workflows and differentiated solutions for scientific researchers and customers.
Serve as an internal reference for system administration and datacenter solutions within the technical community.
Provide technical support to machine learning and deep learning engineers building solutions on NVIDIA technology.

What we're looking for

BS degree in Engineering, Mathematics, Physics, or Computer Science is required (or equivalent experience).
MS or PhD degrees are desirable for this role.
8+ years of experience in accelerated computing for datacenter/HPC-based Enterprise computing solutions.
Proficiency in C, C++, Python, and Bash programming and scripting.
Experience working with the engineering or academic research community supporting high performance computing or deep learning.
Experience with parallel filesystems and understanding of accelerated computing scheduling and I/O stacks.
Skills in Deep Learning frameworks, container technology, and Linux performance tools are preferred.
Strong teamwork, communication, and analytical skills to work in a dynamic environment.

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