1

Nvidia Engineering Jobs in Arizona (NOW HIRING)

Working across Product and Engineering teams. Working with our Operations (customer delivery) and ... NVIDIA, NCR, and Dell. Founded in 2007 and headquartered in Cork, Ireland, Everseen has over 700 ...

Identify high-value AI use cases and guide teams on prompt engineering, model selection, and model ... Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP ...

NVIDIA, Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure; one or more of ... Engineering, Mathematics, or Statistics * 4+ years of experience delivering analytics, machine ...

Data Center Technician

Phoenix, AZ · On-site

$30 - $36/hr

Our network of 1,000+ field engineers operates globally, tackling the most complex deployments in ... NVIDIA, AMD, Intel) Employment Structure & Expectations This is a W-2 hourly, project-based ...

Data Center Technician

Phoenix, AZ · On-site

$30 - $36/hr

Our network of 1,000+ field engineers operates globally, tackling the most complex deployments in ... NVIDIA, AMD, Intel) Employment Structure & Expectations This is a W-2 hourly, project-based ...

Showing results 41-57

Nvidia Engineering information

See Arizona salary details

$43.3K

$136.9K

$162.1K

How much do nvidia engineering jobs pay per year?

As of Sep 5, 2026, the average yearly pay for nvidia engineering in Arizona is $136,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,600.00 and $161,200.00 per year, depending on experience, location, and employer.

What is an Nvidia engineer?

An Nvidia Engineering job involves designing, developing, and optimizing hardware or software solutions in areas such as graphics processing, AI, and high-performance computing. Engineers at Nvidia work on cutting-edge technologies, including GPUs, deep learning frameworks, and system architecture. Roles vary from hardware design and verification to software development and AI research, depending on expertise. Strong skills in programming, computer architecture, and problem-solving are typically required.

What types of projects do Nvidia engineers typically work on, and how is teamwork structured within the engineering department?

Nvidia Engineers commonly engage in projects related to GPU development, AI and deep learning solutions, software driver optimization, and next-generation hardware innovation. Project teams are often multidisciplinary, bringing together software, hardware, and systems engineers to collaborate closely on end-to-end product development. Engineers frequently work in agile, fast-paced environments, attend regular team stand-ups, and participate in cross-functional meetings. This collaborative structure fosters creativity, accelerates problem-solving, and ensures high-quality product delivery while offering team members exposure to diverse technologies and career growth opportunities.

What are the key skills and qualifications needed to thrive as an Nvidia engineer, and why are they important?

To thrive in Nvidia Engineering, candidates typically need strong proficiency in computer engineering, software development, and a solid understanding of hardware architecture, often backed by a relevant degree such as Electrical Engineering or Computer Science. Familiarity with tools like CUDA, C/C++, Python, and version control systems, as well as experience with GPU programming, are highly valued, and certifications such as Nvidia's Deep Learning Institute credentials can enhance a candidate's profile. Excellent problem-solving, team collaboration, and communication skills set top performers apart in this role. These skills and qualifications enable engineers to contribute effectively to complex, innovative projects that drive Nvidia's technological advancements.

What are the most commonly searched types of Nvidia Engineering jobs in Arizona?

The most popular types of Nvidia Engineering jobs in Arizona are:

What cities in Arizona are hiring for Nvidia Engineering jobs?

Cities in Arizona with the most Nvidia Engineering job openings:

Infographic showing various Nvidia Engineering job openings in Arizona as of August 2026, with employment types broken down into 89% Full Time, 8% Part Time, 2% Contract, and 1% Nights. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $136,864 per year, or $65.8 per hour.

Qualification Engineer, Fluidstack Labs

Fluidstack

Glendale, AZ • On-site

$120 - $180/hr

Other

Re-posted 2 days ago


Job description

About Fluidstack

We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.

We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.


We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate
  • Extreme ownership. Full autonomy. Own things end to end often taking on scope outside your core role without being asked to get things done.

  • Velocity. We drive everything forward as fast as possible.

  • First principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.

  • Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.

The Fluidstack Labs Team

Examples of key problems the team is working on

  • Qualify the hardware the frontier runs on before it runs anywhere else. First samples of next-generation accelerators, switches, storage, and liquid cooling land here, and leave as production-ready platforms with runbooks the whole fleet inherits.

  • Compress silicon-to-production to weeks. The lab closes the gap between vendor sample and customer-ready gigawatt infrastructure, and every week cut here pulls the entire 10 GW deployment curve forward.

  • Run the lab like a production site. Provisioning, telemetry, demand management, and liquid cooling mirror production architecture exactly, so a qualification pass in the lab is a deployment guarantee in the field.

  • Prove the power envelope nobody else will touch. Dynamic demand management lets AI compute deploy beyond nominal electrical capacity, and the lab validates the full detection-to-shutdown response chain that makes it safe.


Role Scope
  • Bring up first-sample accelerator platforms (NVIDIA, AMD, custom accelerators) end to end: rack integration, liquid cooling commissioning, firmware baseline establishment, network connectivity, and software stack validation at rack densities up to and beyond 120 kW.

  • Validate network platforms across Broadcom Tomahawk, Broadcom Jericho, and NVIDIA Spectrum silicon, driving Keysight Ixia traffic generation for RFC 2544/2889 benchmarking, line-rate stress, and protocol correctness.

  • Verify the optical layer with EXFO test equipment, covering BER characterization and power budget analysis across the link inventory a qualification depends on.

  • Qualify CDUs and liquid cooling across nVent, CoolIT, and Vertiv platforms: commissioning procedures, BMS telemetry integration, leak detection validation, coolant chemistry verification, and the operational runbooks that fall out of each pass.

  • Exercise the full power oversubscription response chain: graceful and forced shutdown paths, power-cap and p-state levers via BMC, ATS transfer scenarios, breaker-trip detection, rPDU commissioning with outlet-level telemetry, and repeatable power-virus stress harnesses against accelerator hardware.

  • Co-develop qualification matrices with hardware partners, evaluate converged local-NVMe storage platforms (Weka, Hammerspace, VAST Data), and turn results into runbooks production teams inherit.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

  • You've personally brought up servers, accelerators, or switches from first power-on: racking, cabling, firmware, first boot, and everything that goes wrong in between.

  • You script your test harnesses and automation rather than clicking through runs, so your results are repeatable and your throughput compounds.

  • You're deep in Linux and manage hardware at the BMC level: IPMI, Redfish, sensor telemetry, and power and boot control from the command line.

  • You document methodically, producing test reports and runbooks that someone else can execute without you in the room.

  • You've worked hands‑on with liquid‑cooled, high‑power hardware and treat its safety procedures as part of the craft.

  • You debug across hardware, firmware, and software layers yourself, without waiting for someone else to isolate the fault.

  • Bonus: RDMA/RoCEv2 fabrics. Optics testing depth. Kubernetes‑based provisioning stacks. Power and electrical instrumentation experience.

We are committed to pay equity and transparency.

Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.

#J-18808-Ljbffr