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Gpu Computing Jobs (NOW HIRING)

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189.80K/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 ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189.80K/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

OR

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 ...

TensorWave

Head of Customer Experience

Las Vegas, NV · On-site

Recruit, develop, and retain top-tier customer success talent with strong technical backgrounds in AI/ML infrastructure, GPU computing, and cloud platforms * Design scalable processes, runbooks, and ...

Nvidia

OR

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 May 31, 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 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 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 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 jobs make 5000 a week without a degree?

In GPU computing and related tech fields, high-paying roles such as freelance GPU programmer, data scientist, or AI specialist can earn around $5,000 weekly with strong skills and experience. These jobs often require expertise in programming, machine learning, or parallel processing, and may involve contract work or consulting rather than traditional employment.

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.

More about Gpu Computing jobs
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? The top searched job categories for Gpu Computing jobs are:
Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in the United States as of May 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $38,016 per year, or $18.3 per hour.

Senior Software Engineer - CUDA

Snarkify

Palo Alto, CA • On-site, Remote

$144K - $189.80K/yr

Full-time

Posted 7 days ago

Job description

Company Description
At Snarkify, we are passionately driven by our mission to scale zero-knowledge proofs (ZKPs) for a decentralized future. We empower developers by providing robust infrastructure and user-friendly tools, enabling them to effectively build, deploy, and scale ZKP applications. Our operations are steered by a stellar founding team comprising two Ph.D. holders and a Master holder. Our founders carry exceptional expertise gained from renowned organizations such as the Ethereum Foundation, Facebook, Amazon, and Dropbox. Through the application of folding schemes, proof aggregation, and GPU acceleration, we're pushing the scalability of proof systems to unprecedented levels. Through this unwavering commitment, we are shaping a future that promises enhanced security, privacy, and decentralization.
Job Description
Snarkify is seeking an experienced and highly skilled Senior GPU Performance Engineer to join our team and play a pivotal role in advancing the state-of-the-art in Zero-Knowledge Proof (ZKP) acceleration. As a key member of our team, you will have the unique opportunity to shape the future of modern cryptography and decentralized applications by optimizing GPU-based solutions for ZKP applications.
In this role, you will collaborate with our engineering team to identify performance bottlenecks and design innovative GPU-based algorithms and data structures that push the boundaries of ZKP acceleration. 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 capabilities of ZKPs and lay the foundation for a truly decentralized future.
Responsibilities
  • Design, develop, and optimize GPU-based algorithms and data structures to accelerate ZKP proof systems and related computations.
  • Collaborate with the engineering team to identify performance bottlenecks and propose innovative solutions to improve overall system efficiency.
  • Develop and maintain high-quality, well-documented code that adheres to best practices for GPU programming.
  • Analyze and optimize performance across a wide range of GPU architectures, ensuring maximum performance and compatibility.
  • Stay up-to-date with the latest advancements in GPU technology, programming techniques, and industry best practices.
  • Contribute to the creation of technical resources, including whitepapers, tutorials, and presentations, to share knowledge and promote our GPU-based solutions.
  • Mentor junior engineers and provide guidance on GPU-related topics and performance optimization techniques.

Qualifications
  • Bachelor's degree in Computer Science, Computer Engineering, or a related field; Master's or PhD preferred.
  • 2+ years of experience in GPU computing, with a focus on performance optimization and parallel programming.
  • Proficient in GPU programming languages such as CUDA.
  • Strong understanding of computer architecture, memory systems, and parallel algorithms.
  • Experience with profiling and debugging tools for GPU applications, such as NVIDIA Nsight.
  • Familiarity with ZKP applications and blockchain technologies is a plus.
  • Excellent problem-solving skills and ability to analyze complex systems.
  • Strong communication skills, with the ability to work effectively in a collaborative team environment.
  • Demonstrated passion for staying up-to-date with the latest advancements in GPU technology and a commitment to continuous learning.

Additional Information
Benefits
  • Competitive base salary with founding member equity.
  • The opportunity to build the next-generation ZK computing platform.
  • Immersion in a team of top-notch global blockchain engineers.
  • A flexible and innovative remote work environment.
  • Room for continuous growth and development in the ZK field.

Apply