Senior HPC Architect, Automation and At-Scale Deployment

Nvidia

Confirmed live today High trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CATXCOWACAARNCILORUTNMMA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $213k

This role $236k

$154k $302k

below market most similar roles pay here above market

This role pays more than 72% of similar roles. Most pay $180,262–$246,050 — the blue band above. At the midpoint, this role pays about $236k versus about $213k 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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 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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JOB TITLE: Senior HPC Architect, Automation and At-Scale Deployment The Senior HPC Architect, Automation and At-Scale Deployment joins the team to support the deployment and bringup of large-scale GPU compute clusters. This role involves providing engineering solutions to operationalize the latest GPU computing products and software stacks while ensuring technical relationships with internal and external engineering teams. You will implement at-scale system administration and tuning mechanisms for large-scale compute runs, crafting improved workflows for scientific researchers and developers. Key responsibilities include architecting performance platforms and assisting machine learning engineers in building creative solutions. Required skills include C/C++, Python, Bash, and experience with parallel filesystems, accelerated computing scheduling, and I/O stacks. You will work within the domain of high-performance computing and deep learning to solve complex datacenter and GPU-accelerated system challenges.

Skills

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

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.
Architect, develop, and bring up large-scale performance platforms with HPC and OS specialists.
Provide engineering solutions to operationalize the latest GPU computing products and software stacks.
Assist machine learning and deep learning engineers in building creative solutions using NVIDIA technology.
Serve as an internal reference for system administration and large-scale GPU-accelerated system solutions.
Craft improved workflows and develop differentiated solutions for scientific researchers and customers.

What we're looking for

8+ years of experience using accelerated computing for datacenter/HPC-based Enterprise computing solutions.
Solid understanding of accelerated computing scheduling and I/O stacks.
C/C++/Python/Bash programming and scripting experience.
Experience working with engineering or academic research communities supporting high performance computing or deep learning.
Experience with parallel filesystems.
BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience).
MS or PhD (preferred).
Deep Learning framework skills, telemetry/visualization pipelines, container technology, and Linux performance tools (preferred).

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