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Gpu Computing Jobs (NOW HIRING)

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Your expertise in GPU computing, performance optimization, and parallel programming will be instrumental in driving the development of high-performance, energy-efficient solutions that redefine the ...

Nvidia

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

These workloads push the limits of GPU computing, differentiable rendering, computer vision, and production ML systems. In this role, you will help make neural reconstruction faster, more scalable ...

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How much do gpu computing jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for gpu computing in the United States is $18.28, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $19.71 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.

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What states have the most Gpu Computing jobs?

States with the most job openings for Gpu Computing jobs include:

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

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GPU Computing jobs near you
Infographic showing various Gpu Computing job openings in the United States as of August 2026, with employment types broken down into 88% Full Time, 9% Part Time, and 3% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $38,016 per year, or $18.3 per hour.

Senior Software Engineer - cuEquivariance

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 29 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

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. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our group and discover how you can develop a lasting impact on the world.
NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. We are looking for a Senior Software Engineer to join the cuEquivariance team - an NVIDIA library that accelerates geometric neural networks on NVIDIA GPUs, enabling researchers in molecular biology, materials science, and physics to train and deploy equivariant models at scale. This team builds and ships the production GPU kernels and software interfaces that power equivariant deep learning throughout the scientific field. The work spans CUDA kernel engineering, Python library development involving both PyTorch and JAX, and direct collaboration with research teams and external framework developers. If you want to work where GPU computing meets graph-based deep learning, this is the role for you. Your work will run in production pipelines across the scientific community.
What You Will Be Doing:
  • Build, implement, and optimize CUDA kernels for equivariant neural network primitives - tensor products, segmented polynomials, and triangle-based operations - targeting peak performance across NVIDIA GPU generations.
  • Be responsible for the end-to-end delivery of GPU-accelerated geometric ML primitives: from implementation to validated, production-quality software that external frameworks depend on.
  • Build and maintain the interfaces for PyTorch and JAX that expose cuEquivariance primitives to application developers and researchers.
  • Drive CI/CD infrastructure for multi-GPU kernel builds, automated correctness testing, and performance regression tracking.
  • Collaborate with Applied Science and research teams to evaluate new equivariant architectures and translate prototypes into production kernels.
  • Engage directly with third-party framework developers and partners to align on interfaces and ensure delivered software integrates cleanly into production pipelines.

What We Need to See:
  • 6+ years of software engineering experience with a strong background in CUDA and GPU programming.
  • Deep proficiency in C++ and Python; experience building and shipping production libraries used by external developers.
  • Good foundation in GPU computing: memory hierarchy, warp-level execution, occupancy, and performance profiling methodology.
  • Experience building or chipping in to production scientific software libraries, ML frameworks, or developer-facing GPU APIs.
  • Familiarity with concepts in geometric machine learning - equivariance, group representations, irreducible representations, or tensor products - sufficient to work efficiently in the domain.
  • BS/MS in Computer Science, Physics, Applied Mathematics, or a related field, or equivalent experience.

Ways to Stand Out from the Crowd:
  • You have chipped in to or deeply used a major neural network framework that respects equivariance: e3nn, MACE, NequIP, SE(3)-Transformers, or similar.
  • Hands-on experience with Triton kernel development or other GPU kernel authoring tools alongside CUDA.
  • Experience with mixed-precision or tensor-core-aware algorithm design for scientific or ML workloads.
  • PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science with a focus on geometric deep learning or HPC.
  • Contributions to open-source geometric ML or GPU computing projects.

Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
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 for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 21, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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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