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Gpgpu Jobs (NOW HIRING)

NVIDIA

Solutions Architect, Supercomputing

Austin, TX · On-site

$62.50 - $82.25/hr

Preferred : • Experience applying data science to industry problems. • Experience using GPGPU programming and design practices. • CUDA programming and optimization experience. • Background ...

Tencent

Sr. Cloud AI Infrastructure Engineer

Palo Alto, CA · On-site

$151K - $283K/yr

Expertise in GPGPU architectures or other mainstream AI accelerator architectures . 3.Programming & Frameworks: Proficient in parallel computing frameworks; deep understanding of low-level operator ...

Generate

$133K - $175K/yr

We are bringing the many innovations and techniques from state-of-the-art gaming technology (like Proceduralism, GPGPU, Real-Time Graphics, and more) into transforming how the AEC sector designs and ...

Aitech

$100K - $125K/yr

... GPGPU-based AI applications, and advanced cybersecurity for mission-critical systems. Joining us means you'll be working at the forefront of technology with projects that span across land, sea, air ...

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$94.5K

$141.2K

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How much do gpgpu jobs pay per year?

As of Jul 15, 2026, the average yearly pay for gpgpu in the United States is $141,171.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced when optimizing code for GPGPU applications?

Developers working with GPGPU (General-Purpose computing on Graphics Processing Units) often encounter challenges such as managing memory efficiently, ensuring proper synchronization between threads, and maximizing parallelism to fully utilize GPU resources. Debugging and profiling can also be more complex compared to CPU programming due to the highly parallel and asynchronous nature of GPU execution. Collaborating closely with software engineers and hardware specialists is common to address performance bottlenecks and ensure scalable, high-performing applications.

What is the difference between Gpgpu vs GPU Developer?

AspectGpgpuGPU Developer
Required CredentialsKnowledge of parallel programming, CUDA/OpenCLProgramming skills, CUDA/OpenCL, graphics APIs
Work EnvironmentHigh-performance computing, data centersGraphics rendering, game development, visualization
Industry UsageScientific computing, AI, data analysisGaming, multimedia, visualization

Gpgpu (General-purpose computing on graphics processing units) focuses on using GPUs for non-graphics tasks like scientific computing and AI. GPU Developers typically work on graphics rendering, game engines, and multimedia applications. While both roles require knowledge of CUDA or OpenCL, Gpgpu specialists emphasize parallel computing for data processing, whereas GPU Developers focus on graphics and visual effects.

What are the key skills and qualifications needed to thrive as a GPGPU (General-Purpose computing on Graphics Processing Units) Developer, and why are they important?

To excel as a GPGPU Developer, you need strong programming skills in C/C++, parallel computing, and a deep understanding of GPU architectures, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like CUDA, OpenCL, and profiling/debugging tools is typically required, along with relevant certifications being beneficial. Analytical thinking, problem-solving, and the ability to collaborate within multidisciplinary teams are crucial soft skills for this role. These competencies enable developers to optimize computational workloads, improve application performance, and drive innovation in high-performance computing environments.

What is GPGPU?

GPGPU stands for General-Purpose computing on Graphics Processing Units. It refers to the use of a GPU, which is traditionally used for rendering graphics, to perform computation in applications typically handled by the CPU. This approach leverages the parallel processing power of modern GPUs to accelerate tasks such as scientific simulations, data analysis, and machine learning. GPGPU programming often involves using languages like CUDA or OpenCL to write code that runs on the GPU. By offloading certain computations to the GPU, overall performance and efficiency can be significantly improved.
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What cities are hiring for Gpgpu jobs? Cities with the most Gpgpu job openings:
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Gpgpu jobs near you
Infographic showing various Gpgpu job openings in the United States as of July 2026, with employment types broken down into 2% Internship, 94% Full Time, and 4% Contract. Highlights an 96% Physical, and 4% Remote job distribution, with an average salary of $141,171 per year, or $67.9 per hour.
Solutions Architect, Supercomputing

Solutions Architect, Supercomputing

NVIDIA

Austin, TX • On-site

$62.50 - $82.25/hr

Full-time

Posted 21 days ago

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

Job Summary:
NVIDIA is a leading technology company specializing in GPU and networking hardware and software. They are seeking a Solutions Architect to engage with customers and partners, providing technical expertise and support for applications that leverage NVIDIA technology across various industries.
Responsibilities:
• Partner with our industry and account teams in a customer facing role to develop a keen understanding of customer goals, strategies, and technical needs as well as help to define and deliver high-value solutions meeting these needs.
• Document what you know and teach others. This can vary from building targeted samples, to writing white papers, blogs, and wiki articles, to simply working through hard problems with a customer on a whiteboard.
• Be an industry leader with vision on integrating NVIDIA technology into AI Workloads and Workflows, HPC, and enterprise GPU and networking architectures for advanced applications both in the datacenter and at the edge.
• Strategically partner with lighthouse customers and industry-specific solution partners targeting our computing platform.
• We make heavy use of conferencing tools, but some travel is required for this role. Be empowered to find the best way to get your job done and do what it takes to make our partners and customers successful.
Qualifications:
Required:
• Bachelors degree in Engineering, Mathematics, Physics, Computer Science or equivalent experience.
• 5+ years of work-related experience in data science or software development with knowledge of parallel computing with GPUs.
• Background working with modern application deployment practices including but not limited to Slurm, Docker/Containers, and Kubernetes.
• Experience with modern Generative AI and Agentic AI architecture and frameworks.
• Strong written and oral communication skills with the ability to effectively collaborate with accounts, customers, management, and engineering.
• Experience supporting customers/partners in technical engagements.
• Strong organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very complex projects.
• Strong analytical and problem-solving skills.
• This position requires a security clearance and U.S. citizenship.
Preferred:
• Experience applying data science to industry problems.
• Experience using GPGPU programming and design practices.
• CUDA programming and optimization experience.
• Background with network software development.
• Experience with AI: Published record of thought leadership in a technical area or industry segment - Deep Neural Network, Machine Learning R&D - Agentic AI, surrogate Models, or foundation models.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Nvidia employees say

Hours and flexibility

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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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