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Parallel Computing Jobs (NOW HIRING)

Nvidia

Senior GPU Architect, Deep Learning

Durham, NC · On-site

A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for deep learning and parallel processing algorithms. We ...

Pfizer

Design and own robust and dependable high-throughput, parallel, low-latency infrastructure for HPC ... Expertise with distributed computing environments, especially EKS/GKE/Kubernetes * Deep experience ...

Staffingine LLC

PyTorch Role/C++,Python

Bellevue, WA · On-site

Parallel computing models * Memory and compute optimization * Experience with: * PyTorch (model-level workloads and execution) * Triton (custom kernel development / compiler-level interaction)

ConfigUSA

CUDA Programmer

Waukesha, WI · On-site

Strong experience in CUDA programming and parallel computing concepts. * In-depth understanding of NVIDIA GPU architecture (threads, warps, SMs, memory hierarchy). * Proficiency in C/C++ for high ...

AMAX

HPC Engineer

Fremont, CA · On-site

This role requires strong technical experience in HPC architectures, parallel computing, and cluster management, as well as a passion for working with emerging technologies to solve complex problems.

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Parallel Computing information

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$25K

$52.4K

$90.5K

How much do parallel computing jobs pay per year?

As of Jun 21, 2026, the average yearly pay for parallel computing in the United States is $52,360.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,000.00 and $59,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Parallel Computing Specialist, and why are they important?

To thrive as a Parallel Computing Specialist, you need strong knowledge of computer architecture, parallel algorithms, and experience with programming languages such as C/C++, Python, and frameworks like MPI or OpenMP, often supported by a degree in computer science or a related field. Familiarity with high-performance computing (HPC) environments, GPU programming (CUDA, OpenCL), and cloud-based parallel processing systems is typically required. Analytical thinking, problem-solving abilities, and effective collaboration are crucial soft skills in this role. These skills are vital for efficiently designing, optimizing, and implementing solutions that leverage parallelism to significantly accelerate computational tasks.

What are some common challenges faced by professionals working in parallel computing roles?

Professionals in parallel computing often encounter challenges such as efficiently dividing complex tasks among multiple processors and minimizing communication overhead between them. Debugging and optimizing performance across parallel architectures can be difficult, as issues like race conditions and load imbalances frequently arise. Additionally, staying current with evolving hardware technologies and parallel programming frameworks is essential to ensure solutions remain efficient and scalable. Collaborating with cross-functional teams, such as data scientists and system architects, is also crucial for integrating parallel solutions into larger projects.

What is the difference between Parallel Computing vs Data Analyst?

AspectParallel ComputingData Analyst
Required CredentialsComputer Science or Engineering degree, programming skillsStatistics, Data Science, or related degree, analytical skills
Work EnvironmentResearch labs, tech companies, high-performance computing centersBusiness, finance, healthcare, corporate offices
Industry UsageTechnology, research, scientific computingBusiness intelligence, market analysis, reporting

While Parallel Computing focuses on developing algorithms to process large data sets efficiently across multiple processors, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills but serve different purposes: one enhances computational performance, the other informs business decisions.

What is parallel computing?

Parallel computing is a type of computation where many calculations or processes are carried out simultaneously, leveraging multiple processors or computers to solve complex problems more efficiently. It divides large tasks into smaller ones that can be executed concurrently, significantly speeding up processing time. Commonly used in scientific research, data analysis, and engineering, parallel computing is essential for handling large-scale simulations and big data applications.
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Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in the United States as of June 2026, with employment types broken down into 84% Full Time, and 16% Part Time. Highlights an 74% Physical, 6% Hybrid, and 20% Remote job distribution, with an average salary of $52,360 per year, or $25.2 per hour.
Senior GPU Architect, Deep Learning

Senior GPU Architect, Deep Learning

Nvidia

Santa Clara, CA

Full-time

Posted 18 days ago

Job description

We are now looking for a Senior GPU & Deep Learning Architect!

The NVIDIA GPU Architecture group is looking for world class architects and software developers to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for deep learning and parallel processing algorithms. We are constantly looking for ways to improve our GPU architecture, especially for deep learning workloads, both training and inference, and maintain our leadership by developing new parallel programming models, and new architectures required to make this successful. In this position, you will be responsible for developing and enhancing various features in the GPU architecture that advance the state of the art in parallel programming models or parallel computing performance. You would interact with other world-class architects and researchers to build simulators, mapping deep learning workloads to current and future hardware, and validate new architectural features.

What you'll be doing:

  • Design new hardware features for future processing architectures targeted at deep learning workloads, for both training and inference.

  • Advance the state of parallel computation.

  • Be knowledgeable about future parallel programming models and their impact to hardware.

  • Develop software for various hardware simulators, test infrastructures or metrics systems including databases.

  • Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models.

  • Develop tests, testplans, and testing infrastructure for new graphics or parallel processing architectures

  • Be hungry to learn and work on simulators, RTL and real silicon.

What we need to see:

  • MS in Computer Science, Electrical Engineering or Computer Engineering or equivalent experience.

  • Experience in working with hardware targeted at deep learning, or working on mapping deep learning algorithms to hardware.

  • 8+ years of relevant industry experience in GPU or other parallel programming architectures (or other equivalent experience).

  • Strong programming ability inC, C++, Perl andPython.

  • Background in computer architecture, parallel processing, signal processing and/or high performance computing.

  • Knowledge of state of the art in DL algorithms and attention mechanisms is a huge plus.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hard working people in the world working for us. If you're creative, autonomous, and love a challenge, consider joining our GPU Architecture team and help us build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.

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 January 13, 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.
Nvidia logo

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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