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Gpu Computing Jobs in California (NOW HIRING)

Nvidia

Senior Hardware Validation Engineer

Santa Clara, CA · On-site

$129K - $173K/yr

More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that ...

Nvidia

Senior Systems Performance Engineer

Santa Clara, CA · On-site

$122K - $167K/yr

More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that ...

YO AI Labs

GPU Programmer - Remote

Los Angeles, CA · Remote

$60 - $85/hr

Background in graphics programming, ML acceleration, scientific computing, HPC, or related GPU-focused fields. * Strong understanding of GPU architecture and performan

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YO AI Labs

GPU Programmer - Remote

San Jose, CA · Remote

$60 - $85/hr

Background in graphics programming, ML acceleration, scientific computing, HPC, or related GPU-focused fields. * Strong understanding of GPU architecture and performan

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Gpu Computing information

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$9

$18

$24

How much do gpu computing jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for gpu computing in California is $18.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.95 and $19.47 per hour, depending on experience, location, and employer.

What is GPU computing?

GPU computing refers to the use of a Graphics Processing Unit (GPU) alongside a Central Processing Unit (CPU) to accelerate computational tasks. GPUs are highly efficient at performing parallel operations, making them ideal for complex calculations in fields like machine learning, scientific simulations, and graphics rendering. Unlike traditional CPUs, GPUs can process thousands of threads simultaneously, greatly speeding up tasks that involve large-scale data processing. This makes GPU computing essential in industries requiring high-performance computing solutions.

What are some common challenges faced by GPU computing professionals when optimizing code for parallel processing?

One of the main challenges in GPU Computing is efficiently restructuring code to leverage the massive parallelism that GPUs offer. Professionals often encounter issues with memory management, synchronization between threads, and minimizing data transfer between CPU and GPU to avoid bottlenecks. Additionally, debugging parallel code can be complex, as errors may not manifest consistently across runs. Collaborating with software engineers, data scientists, and hardware specialists is typical to ensure optimal performance and scalability in real-world applications.

What are the key skills and qualifications needed to thrive as a GPU computing specialist, and why are they important?

To thrive as a GPU Computing Specialist, you need expertise in parallel programming, computer architecture, and a strong foundation in mathematics and algorithms, often supported by a degree in computer science, engineering, or related fields. Familiarity with programming languages like C/C++, CUDA, OpenCL, and experience with GPU hardware and high-performance computing systems are essential. Problem-solving abilities, analytical thinking, and strong collaboration skills help you innovate and work effectively on complex computational projects. These skills ensure efficient development, optimization, and deployment of GPU-accelerated solutions crucial for scientific, engineering, and AI applications.

What is the difference between Gpu Computing vs Data Scientist?

AspectGpu ComputingData Scientist
Required CredentialsKnowledge of GPU architectures, programming skills in CUDA or OpenCLDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentHigh-performance computing environments, data centers, research labsOffice settings, research institutions, tech companies
Industry UsageMachine learning, scientific simulations, graphics renderingData analysis, predictive modeling, business insights

Gpu Computing focuses on leveraging GPU hardware for high-speed processing tasks, often requiring specialized programming skills. Data Scientists analyze data to extract insights, using various tools and statistical methods. While both roles involve data and computing, Gpu Computing is more hardware and performance-oriented, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Gpu Computing jobs in California?

For Gpu Computing jobs in California, the most frequently searched job titles are:

What job categories do people searching Gpu Computing jobs in California look for?

The top searched job categories for Gpu Computing jobs in California are:

Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in California as of August 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $37,518 per year, or $18 per hour.

Senior GPU Compiler Development Engineer

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 26 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 246 rated software companies

Job description

We are looking for experienced Systems SW Compiler Engineers for an exciting role in our PTX (Parallel Thread Execution) Compiler Development team. Join the PTX Compiler team and help drive PTX language design and PTX compiler evolution. PTX enables all GPU Computing applications including HPC, Deep Learning and Autonomous Driving. PTX provides a stable programming model and portable instruction set Architecture (ISA) for NVIDIA GPUs and used by all Compute programming languages compiled to NVIDIA GPUs. PTX is also used as a compiler target by various non-NVIDIA compilers. Work with NVIDIA GPU Architecture and CUDA Programming model teams to build abstractions to expose new GPU features in portable and performant ways in PTX ISA. PTX Compiler (PTXAS) apart from implementing PTX ISA is responsible for PTX Compiler Front End, interaction with optimizer and runtime aspects involving object files, debug information, linkers, loaders and Driver Compiler Interface.
As a senior member of the team, you will be responsible for leading efforts to enhance PTX Compiler infrastructure to enhance it to support new compilation models for DL and Generative AI codes. You will be contributing towards evolving programming model for Generative AI and DL applications on GPUs.
What you will be doing:
  • Provide stewardship for PTX ISA and PTX Compiler infrastructure for Generative AI and DL.
  • Collaborating with architecture and programming model teams to design and implement programming models for next generation GPUs.
  • Working closely with others to help design compilation stack and strategies for AI and DL workloads.
  • Collaborate closely with teams developing other related components to ensure compatibility, robustness and high-quality code generation.

What we need to see:
  • BS (or equivalent experience), MS or Ph.D. in Computer Science, Computer Engineering, or related fields.
  • 6+ years of experience in the area of compiler front end, programming language designs, Compilers/Linkers.
  • Superb analytical and C/C++ programming skills.
  • Able to expertly use AI tools and maintain AI generated artifacts
  • Experience in any one area of compiler development including feature support, code generation and compiler infrastructure.
  • Excellent and strong interactive, verbal and written communications skills.
  • Understanding of any Processor ISA (GPU ISA a plus).
  • Good track record of developing, driving and delivering software products.

Ways to stand out from the crowd:
  • Experience in Programming Languages design and drafting programming language standards.
  • Knowledge of GPU development and compute APIs such as CUDA, and OpenCL.
  • Development experience in LLVM IR, MLIR

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most brilliant and hardworking people in the world working with us and our product lines are growing fast in some of the hottest state of the art fields such as Deep Learning, Artificial Intelligence, Autonomous Vehicles, Virtual Reality, etc. Our diverse team of talented, capable, and professional people are our greatest asset! 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 184,000 USD - 287,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 3, 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.

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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