Full-Time

Senior HPC Storage Engineer

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$184k - $356.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Austin, TX, USA + 1 more

More locations: Santa Clara, CA, USA

In Person

Bachelor's

Category
Data & Analytics (1)
Required Skills
Bash
Python
Docker
Linux/Unix

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Requirements
  • Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
  • 8+ years of experience designing and/or operating large scale storage infrastructure.
  • Experience analyzing and tuning storage performance for a variety of workloads.
  • Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting
  • In depth understanding of container technologies like Docker, Enroot
Responsibilities
  • Research and analyze existing internal distributed storage services.
  • Research, design, and implement scalable, next-gen distributed storage services for HPC workloads, optimizing both performance and cost-effectiveness to meet NVIDIA’s growing infrastructure needs
  • Develop tooling to automate management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources.
  • Detail the general procedures and practices, perform technology evaluations, related to distributed file systems.
  • Collaborate across teams to better understand developers' workflows and capture their infrastructure requirements.
  • Influence and guide methodologies for building, testing, and deploying applications to ensure efficient performance and resource utilization.
  • Supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows
  • Root cause analysis and suggest corrective action for problems large and small scales
Desired Qualifications
  • Distributed Storage Expertise: Extensive experience with parallel and distributed filesystems (Ceph, Weka.io, Vast, Lustre, GPFS) and Linux storage kernel development.
  • GPU & AI Infrastructure: Proficient with NVIDIA GPUs, CUDA programming, and NCCL, including performance benchmarking via MLPerf.
  • Hardware & Storage Engineering: Deep familiarity with storage hardware (HDDs, SSDs, NVMe), enclosures, and specialized appliances like Network Appliance.
  • Advanced Networking: Strong background in Software Defined Networking (SDN) and high-performance networking for AI/HPC clusters.
  • Deep Learning Frameworks: Practical experience applying industry-standard frameworks, specifically PyTorch and TensorFlow.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • NVIDIA reported $215.9 billion fiscal 2026 revenue, up 65%, on February 25, 2026.
  • The August 10, 2026 Wall Street financing pact opens more buyers for NVIDIA hardware.
  • Nemotron 3.5 Lightning boosts ecosystem lock-in while driving cheap GPU demand.

What critics are saying

  • US Commerce tightened China chip controls again on May 31, 2026.
  • The $500 billion financing push ties growth to GPU resale values and customer defaults.
  • An AI hardware glut from AMD, Huawei, or Chinese foundries crushes collateral and pricing.

What makes NVIDIA unique

  • CUDA remains the default software moat for AI training and deployment.
  • NVIDIA secured SK Hynix as its largest memory partner in June 2026.
  • Vera Rubin and Blackwell keep NVIDIA ahead in rack-scale AI systems.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-2%

2 year growth

-3%
CNBC
Aug 12th, 2026
Nvidia taps Wall Street to raise $500B for AI infrastructure buildout

Nvidia has partnered with six US asset managers willing to raise $500 billion to finance AI infrastructure development. The chip giant is positioning AI infrastructure as a new asset class, with the plan hinging on GPUs retaining value over time like traditional hard assets rather than depreciating electronics. The approach carries risks. Ben Emons of FedWatch Advisors warned that Chinese manufacturers could flood markets with low-cost chips, potentially causing hardware prices to collapse and eroding collateral backing billions in private loans. Nvidia also launched Nemotron 3.5 Lightning, its first open-source AI model since CEO Jensen Huang advocated for open models. The lightweight model runs on a single GPU, potentially boosting chip sales by offering cheaper alternatives to proprietary models. Meanwhile, oil prices rose over 6% this week as prospects dimmed for a deal to increase traffic through the Strait of Hormuz.

Yahoo Finance
Aug 11th, 2026
Musk's 10GW SpaceX data centre plan could generate $300B in Nvidia orders or expose dangerous concentration risk

Elon Musk has announced plans to scale SpaceX data centres from 1.4 gigawatts to 10 gigawatts by 2027, working exclusively with NVIDIA hardware. Research firm SemiAnalysis estimates this could generate $150 billion to $500 billion in capital spending. The move could push NVIDIA shares towards $500, building on its $5.42 trillion market capitalisation and 92% data centre revenue share. NVIDIA recently announced a $500 billion financing partnership with Apollo, BlackRock, and other major firms to support AI infrastructure buildouts. However, the proposal creates significant concentration risk. SpaceX would propose capital expenditure rivalling Amazon Web Services and Google combined, whilst being far less profitable. NVIDIA already holds $119 billion in supply commitments. If SpaceX funding tightens or hyperscale customers slow orders, the stock could face substantial downside risk. Meanwhile, AMD has surged 121% year-to-date versus NVIDIA's 17% gain.

Cointime
Aug 11th, 2026
AI startup Trajectory raises $40M at $300M valuation led by Sequoia Capital

AI infrastructure startup Trajectory has raised $40 million at a $300 million post-money valuation, led by Sequoia Capital with participation from Nvidia and Bessemer, according to The Information. The funding comes just two months after the company secured a $15 million seed round at a $115 million valuation. Founded in May by former Google DeepMind researchers Ronak Malde and Michael Elabd, alongside ex-Apple researcher Arjun Karanam, Trajectory focuses on continuous learning technology. The platform transforms user corrections, retries and edits into training signals, enabling AI models to improve after deployment. The company automates this process, allowing enterprises to continuously adjust models, prompts and harnesses based on real usage data. Clay, Decagon and Harvey are currently using or testing the technology.

Yahoo Finance
Aug 11th, 2026
Nvidia develops Nemotron 4 open-source AI model with 1T+ parameters

Nvidia is developing Nemotron 4, a new AI model family aimed at rivaling top open-source models globally, The Information reported. The largest model is expected to have at least 1 trillion parameters, according to employees working on the project. Nvidia has not set a release date, though the model could be ready as early as late autumn. The company has yet to complete final training. Separately, Nvidia unveiled Nemotron 3.5 Lightning for tasks including code review and security monitoring. It also released NeMo Switchyard, an open-source model-routing library. The chip giant is among few major US firms releasing open-source models, which have gained attention as AI costs rise and Chinese models approach capabilities of systems from Anthropic and OpenAI.

CNBC
Aug 11th, 2026
Nvidia releases first open-source AI model after CEO Huang's open letter debut

Nvidia has released Nemotron 3.5 Lightning, its first open-source AI model since CEO Jensen Huang entered the open-source AI debate. The model was developed particularly for autonomous AI agents and will be available on HuggingFace and Nvidia's website. Huang previously argued that open-weight models allow companies greater control, spur competition, and bring down pricing. For Nvidia, open-source AI boosts chip sales, as the models still require GPUs to run. Companies including CodeRabbit and Harvey have tested the model. Nvidia also released NeMo Switchyard software to determine the most appropriate and cost-effective AI model for specific tasks. Nvidia used distillation techniques to give Nemotron 3.5 Lightning capabilities similar to its larger models.