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Internship Nvidia Hardware Engineer Jobs in Utah

Tractable

Embedded Systems Engineer

Salt Lake City, UT · On-site

$120/hr

We're looking for an Embedded Systems Engineer to help build and evolve the device-side (edge ... The role sits at the intersection of embedded software, hardware integration, and sensor/vision ...

Moxtek

Software Engineer - Automation and Controls

Orem, UT · On-site

$70K - $78K/yr

Design, implement, test, and debug software that communicates with hardware devices using Ethernet ... internships, co-op experience, or relevant academic projects.Location:This role requires on-site ...

Moxtek

Software Engineer - Automation and Controls

Orem, UT · On-site

$70K - $78K/yr

Design, implement, test, and debug software that communicates with hardware devices using Ethernet ... internships, co-op experience, or relevant academic projects.Location:This role requires on-site ...

Moxtek

Software Engineer - Automation and Controls

Orem, UT · On-site

$39.25 - $50.25/hr

Design, implement, test, and debug software that communicates with hardware devices using Ethernet ... including internships, co-op experience, or relevant academic projects. Location: This role ...

Moxtek

Software Engineer - Automation and Controls

Orem, UT · On-site

$39.25 - $50.25/hr

Design, implement, test, and debug software that communicates with hardware devices using Ethernet ... including internships, co-op experience, or relevant academic projects. Location: This role ...

Cosm

General Interest - Internship

Dallas, TX · On-site

$14.75 - $19.75/hr

DESIGN ENGINEERING - Chadds Ford, PA Get hands-on experience with our Engineering team developing ... You could work on anything from QA and media playback to full-stack tools and hardware integrations.

Raytheon

RF Data Link Engineer II

Provo, UT

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

At RTX, our internships, co-ops and full-time careers provide an exceptional foundation to work on ... Data link hardware development * Algorithm development * Digital signal processing What You Will Do

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Internship Nvidia Hardware Engineer information

What is the difference between Internship Nvidia Hardware Engineer vs Nvidia Hardware Engineer?

AspectInternship Nvidia Hardware EngineerNvidia Hardware Engineer
QualificationsEnrolled in a relevant degree program, some technical courseworkBachelor's or Master's in Electrical Engineering, Computer Engineering, or related fields
Work EnvironmentInternship program, mentorship, collaborative teamsFull-time, professional environment, project ownership
ResponsibilitiesAssist in hardware design, testing, and documentationDesign, develop, and optimize hardware components and systems

Internship Nvidia Hardware Engineers are students gaining practical experience, focusing on learning and assisting with hardware projects. Nvidia Hardware Engineers are full-time professionals responsible for designing and developing advanced hardware solutions. The internship offers a pathway to a career in hardware engineering, while full-time roles involve more responsibility and expertise.

What are the key skills and qualifications needed to thrive as an internship Nvidia hardware engineer?

To thrive as an Internship Nvidia Hardware Engineer, you need a solid understanding of electrical engineering fundamentals, digital/analog circuit design, and relevant coursework or project experience. Familiarity with hardware description languages (such as Verilog or VHDL), simulation tools (like ModelSim or Cadence), and version control systems is typically expected. Strong problem-solving abilities, effective teamwork, and clear communication distinguish top candidates in this role. These skills and qualities are crucial to efficiently contribute to complex hardware development projects and collaborate within multidisciplinary engineering teams.

What does an internship Nvidia hardware engineer do?

An Internship Nvidia Hardware Engineer works alongside experienced engineers to assist in designing, testing, and validating hardware components such as GPUs, processors, and related systems. Interns typically get hands-on experience with circuit design, simulation, debugging, and performance analysis. They may also collaborate with cross-functional teams to optimize hardware for efficiency and performance. This role gives students exposure to industry-standard tools and real-world engineering challenges, preparing them for future careers in hardware engineering.

What kinds of projects can an intern expect to work on as a hardware engineer at Nvidia?

As a Hardware Engineer intern at Nvidia, you can expect to be involved in hands-on projects that support the development, testing, and validation of hardware components such as GPUs, system boards, or AI accelerators. Interns often collaborate closely with experienced engineers, contributing to tasks like schematic reviews, board bring-up, signal integrity analysis, or automation of testing procedures. These projects provide opportunities to learn industry-standard tools and workflows, as well as gain practical experience in problem-solving and cross-functional teamwork. This exposure not only builds technical skills but also helps interns understand the end-to-end hardware development lifecycle.

What are the most commonly searched types of Nvidia Hardware Engineer jobs in Utah?

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Internship Nvidia Hardware Engineer jobs near you

Senior Deep Learning Tools Engineer - CUDA Tile

Nvidia

Salt Lake City, UT

$101K - $138K/yr

Full-time

Re-posted 8 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 244 rated software companies

Job description

NVIDIA is building advanced compiler technologies to accelerate AI workloads, and we are looking for an engineer focused on performance validation, analysis, and tracking. In this role, you will work at the intersection of deep learning compilers, GPU systems, and automation infrastructure, ensuring that performance improvements are measurable, scalable, and continuously validated over time.

Do you want to help drive the performance of next-generation compilers? Are you excited by how GPU performance powers breakthroughs in deep learning, autonomous systems, and high-performance computing? We are seeking a talented Deep Learning Compiler & Tools Engineer focused on CUDA Tile (Performance & Infrastructure) to join our team.

You will collaborate closely with compiler developers, infrastructure providers, and hardware teams to build systems that track, analyze, and improve performance across rapidly evolving AI workloads. If you're passionate about performance, systems, and building infrastructure that drives real-world impact, we want to hear from you.

What You'll Be Doing:

  • Design and develop performance testing frameworks for deep learning compilers and workloads

  • Build and maintain automated pipelines (CI/CD) to continuously track performance across models, hardware, and compiler changes

  • Implement benchmarking systems to measure latency, throughput, and efficiency of AI and HPC workloads

  • Analyze performance trends over time and identify regressions, bottlenecks, and optimization opportunities

  • Partner with compiler and architecture teams to debug and resolve performance issues

  • Develop tools and dashboards for performance visualization, reporting, and insights

  • Enable scalable testing across diverse GPU systems and environments

  • Improve infrastructure to ensure reliable, reproducible, and high-signal performance data

What We Need to See:

  • BS, MS, or PhD (or equivalent experience) in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or related field

  • 5+ years of software engineering experience, including experience in performance engineering, benchmarking, or systems optimization

  • Strong programming skills in Python (C++ is a plus)

  • Experience with CI/CD systems and automation frameworks

  • Familiarity with hardware-aware performance analysis (GPUs, accelerators, or similar systems)

  • Experience working with deep learning frameworks such as PyTorch, TensorFlow, JAX, or TensorRT

  • Background in data analysis, profiling, and regression tracking

  • Ability to debug complex system-level issues across software and hardware layers

Ways to Stand Out from the Crowd::

  • Experience with GPU performance analysis and optimization

  • Understanding of compiler internals (LLVM, MLIR, CUDA compilation flow)

  • Experience building performance dashboards and large-scale telemetry systems

  • Familiarity with hardware/software co-design or low-level performance tuning

  • Experience with distributed testing infrastructure or large-scale benchmarking systems

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered one of the most desirable employers in the technology industry. Our teams are tackling some of the most challenging problems in AI, deep learning, and accelerated computing.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 10, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearning

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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