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NVIDIA

Designs the chips and software that power AI

Solutions Architect - Networking Ethernet

Full-Time
No salary listed
Senior, Expert
Bachelor's, Master's, PhD
Remote in Australia
RemoteMelbourne or Sydney preferred; remote work from Australia.
Solution EngineeringImplementation & Technical Account Management

About the job

Requirements
  • A Bachelor of Science, Master of Science, or Doctor of Philosophy or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields.
  • At least five or more years of professional experience in networking fundamentals, TCP/IP stack, and data center architecture.
  • Proficiency in configuring, testing, validating, and resolving issues in local area networks, especially in medium to large-scale high-performance computing and artificial intelligence environments.
  • Advanced knowledge of Ethernet Virtual Private Network, Border Gateway Protocol, Open Shortest Path First, and Virtual Extensible LAN protocols.
  • Hands-on experience with network switch and router platforms such as Cumulus Linux, SONiC, IOS, Junos OS, and EOS.
  • Extensive experience delivering automated network provisioning solutions using Ansible, Salt, and Python.
  • Ability to develop continuous integration and continuous deployment pipelines for network operations.
  • Strong focus on customer needs and satisfaction.
  • Self-motivated with leadership skills to work collaboratively with customers and internal teams.
  • Strong written, verbal, and listening skills in English.
Responsibilities
  • Building and operating AI/HPC infrastructure for new and existing customers.
  • Supporting operational and reliability aspects of large-scale AI clusters, focusing on performance at scale, real-time monitoring, logging, and alerting.
  • Engaging in and improving the whole lifecycle of services—from inception and design through deployment, operation, and refinement.
  • Maintaining services once they are live by measuring and monitoring availability, latency, and overall system health with emphasis on performance and availability optimisations to meet requirements and service level agreements.
  • Providing feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements.
Desired Qualifications
  • Experience with NVIDIA Spectrum networking in large-scale deployments.
  • Familiarity with cloud networks such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
  • Background with RDMA-based fabrics such as InfiniBand or RoCE in HPC or AI environments.
  • Experience with high-performance computing architectures, understanding of Kubernetes and container-based microservices and how job schedulers (Slurm, PBS) work.
  • Cluster management technologies knowledge with bonus credit for Base Command Manager and/or Run.AI, as well as experience with GPU-focused hardware and software such as NVIDIA DGX, CUDA, Network/GPU Operator.

About the company

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify's Take

What believers are saying

  • AI demand is still surging: data center sales rose 117% to $89 billion in the quarter ending July 2026.
  • Stock grants are worth more: shares hit a record in October 2026, up 28% for the year.
  • Engineering dominates hiring: 1,722 of NVIDIA's 2,686 job listings in October 2026 were in engineering.

What critics are saying

  • Big buyers are hedging: Meta's February 2026 agreement covers 6 gigawatts of AMD chips.
  • Financing its buyers is getting harder: lenders pushed back on NVIDIA's $500 billion chip-backed loan plan in October 2026.
  • Staff described long hours: Bloomberg's August 2024 interviews reported seven-day weeks, late nights and shouting matches.

What makes NVIDIA unique

  • Rivals must match its software too: over 7.5 million developers use CUDA and other NVIDIA tools to program its chips.
  • The CEO has a broad direct staff: Jensen Huang says over 60 people report to him, without one-on-ones.
  • People rarely leave: NVIDIA's yearly staff turnover is 3.7%, versus a 21.9% average across the tech industry.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-1%

2 year growth

-2%
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Two weeks before ChatGPT launched, Nvidia's earnings call for the third quarter of fiscal 2023 held early signs of the generative AI boom. The November 16, 2022 report looked weak: revenue fell 17% year over year, gaming sales halved to $1.6 billion, and the company took a $702 million inventory charge tied to new US export rules on China. Yet data center revenue rose 31% to a record $3.8 billion, nearly two-thirds of the total. Management cited large language models and generative AI as growth drivers, and chief executive officer Jensen Huang called it "the beginning of the era of generative AI." Nvidia also began shipping its H100 data center chip that quarter. The stock was worth roughly $390 billion then, and $5.5 trillion in October 2026.

Yahoo Finance
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NVIDIA announced on October 14, 2026 that semiconductor leaders are adopting its CUDA-X libraries to build AI-defined fabs, factories where AI helps engineers optimise processes and fix production issues. The news came at SEMICON West in San Francisco. The company introduced cuPotential, a library for building AI agents that simulate how atoms and molecules interact. In early-access testing, it sped up already GPU-optimised workflows by an additional 2-3x. Synopsys is among the early users. Lam Research expects NanoVDB, an open source 3D data library, to speed up some wafer-etch modelling by up to 30x. Applied Materials, Cadence, KLA, and Siemens are also using NVIDIA's tools across chip design, manufacturing, and testing.