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Junior Nvidia Engineering Jobs (NOW HIRING)

Nvidia

Senior Circuit Design Engineer

Santa Clara, CA · Hybrid

NVIDIA stands at the intersection of hardware excellence and AI breakthrough, where every line of ... junior team members. What we need to see: * BSEE/MS preferred in Electrical, Computer Engineering ...

Nvidia

Senior Robotics Research Scientist

Seattle, WA

$112K - $142K/yr

Collaborating with product managers and engineering teams to transfer your research into NVIDIA products that will have real-world impact; * Mentoring interns and more junior research scientists and ...

Nvidia

Senior Circuit Design Engineer

Santa Clara, CA · Hybrid

$122K - $164K/yr

Over the past two decades, NVIDIA has continuously reinvented itself, starting with the ... Be a mentor/technical lead for junior team members. What we need to see: * BSEE (or equivalent ...

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Junior Nvidia Engineering information

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$33.5K

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$109.5K

How much do junior nvidia engineering jobs pay per year?

As of Jul 3, 2026, the average yearly pay for junior nvidia engineering in the United States is $71,799.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,500.00 and $80,000.00 per year, depending on experience, location, and employer.

What is a Junior Nvidia Engineer?

A Junior Nvidia Engineer is an early-career professional who works with Nvidia technologies, such as GPUs, AI hardware, and related software development kits. Their responsibilities typically include assisting in the design, development, testing, and optimization of software or hardware solutions utilizing Nvidia platforms. They often collaborate with senior engineers to solve technical challenges, support product development, and learn about advanced computing technologies. This role is ideal for those interested in graphics processing, machine learning, and high-performance computing. Junior Nvidia Engineers usually have a background in computer science, electrical engineering, or a related field.

What are the key skills and qualifications needed to thrive as a Junior Nvidia Engineer, and why are they important?

To thrive as a Junior Nvidia Engineer, you need a solid grounding in computer science principles, programming (especially in C++ and Python), and a relevant degree such as Computer Engineering or Electrical Engineering. Familiarity with Nvidia's CUDA platform, GPU architectures, and common development tools like Git and Linux is typically required. Strong problem-solving skills, effective teamwork, and a willingness to learn new technologies are crucial soft skills in this role. These abilities are essential to contribute to innovative hardware and software solutions, collaborate effectively, and adapt to the rapid advancements in GPU technology.

What are some common challenges faced by junior engineers at Nvidia, and how can they overcome them?

As a junior engineer at Nvidia, you may encounter challenges such as adapting to a fast-paced environment, learning proprietary technologies, and collaborating with cross-functional teams. It's common to feel overwhelmed by the complexity of projects and the high expectations for innovation. To overcome these hurdles, proactively seek mentorship from experienced colleagues, participate in internal training sessions, and regularly communicate with your team to clarify goals and expectations. Building strong technical foundations and asking questions when you need support can help you grow quickly in this dynamic environment.

What is the difference between Junior Nvidia Engineering vs Junior Data Scientist?

AspectJunior Nvidia EngineeringJunior Data Scientist
Required CredentialsBachelor's in Computer Science, Electrical Engineering, or related fields; knowledge of CUDA, GPU architectureBachelor's or Master's in Data Science, Statistics, or related fields; programming in Python, R, SQL
Work EnvironmentHardware-focused, engineering labs, GPU development teamsData analysis teams, research environments, software development
Industry UsageTechnology, hardware manufacturing, AI hardware accelerationTech, finance, healthcare, research institutions
Common Search/ComparisonYesYes

Junior Nvidia Engineers focus on GPU hardware, CUDA programming, and hardware development, often working in engineering labs. In contrast, Junior Data Scientists analyze data, develop models, and work with statistical tools. Both roles require strong programming skills but differ in their core focus and industry applications.

More about Junior Nvidia Engineering jobs
What cities are hiring for Junior Nvidia Engineering jobs? Cities with the most Junior Nvidia Engineering job openings:
What are the most commonly searched types of Nvidia Engineering jobs? The most popular types of Nvidia Engineering jobs are:
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Junior Nvidia Engineering jobs near you
Infographic showing various Junior Nvidia Engineering job openings in the United States as of June 2026, with employment types broken down into 80% Full Time, and 20% Part Time. Highlights an 88% Physical, 6% Hybrid, and 6% Remote job distribution, with an average salary of $71,799 per year, or $34.5 per hour.
Senior Deep Learning Compiler Engineer - XLA

Senior Deep Learning Compiler Engineer - XLA

Nvidia Corporation

Santa Clara, CA • On-site

$122K - $168K/yr

Full-time

Posted 8 days ago

Job description

NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as "the AI computing company".
We are looking for versatile software engineers for our XLA team. NVIDIA is at the center for the AI revolution that's transforming how people live, work, and interact with technology. Come join us to build high-performance, production-grade software that's at the core of next-generation AI systems.
What you will be doing:
In this role, develop compiler optimization algorithms for deep learning workloads. You will optimize inference and training performance for the JAX framework and the OpenXLA compiler on NVIDIA GPUs at scale. You'll collaborate with our partners in deep learning framework teams and our hardware architecture teams to accelerate the next generation of deep learning software. The scope of these efforts include:
  • Crafting and implementing compiler optimization techniques for deep learning network graphs.
  • Designing novel graph partitioning and tensor sharding techniques for distributed training and inference.
  • Performance tuning and analysis.
  • Code-generation for NVIDIA GPU backends using open-source compilers such as MLIR, LLVM and OpenAI Triton.
  • Designing user facing features in JAX and related libraries and other general software engineering work.
  • Working closely with GPU hardware engineering teams to design AI compiler software features for next-generation GPUs.

What we need to see:
  • Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field (or equivalent experience).
  • 4+ years of relevant work or research experience in performance analysis and compiler optimizations.
  • Ability to work independently, define project goals and scope, and lead your own development effort adopting clean software engineering and testing practices.
  • Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.
  • Strong foundation in architecture of CPU, GPUs or other high performance hardware accelerators. Knowledge of high-performance computing and distributed programming.
  • CUDA or OpenCL programming experience is desired but not required.
  • Experience with the following technologies is a huge plus: XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and deep learning framework design.
  • Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team. A history of mentoring junior engineers and interns is a bonus.

Ways to stand out from the crowd:
  • Experience working deep learning frameworks such as JAX, PyTorch or TensorFlow.
  • Extensive experience with CUDA or with GPUs in general.
  • Experience with open-source compilers such as XLA, LLVM, MLIR or TVM.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
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 March 1, 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.
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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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