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Parallel Computing Jobs (NOW HIRING)

Nvidia

Senior GPU System Architect

Santa Clara, CA · Hybrid

Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming ...

GIGATEC

HPC Software Engineer

Annapolis Junction, MD · On-site

$235K - $265K/yr

This position involves working with parallel computing frameworks , optimizing algorithms for parallel execution, and ensuring scalability and performance of software applications on HPC clusters!

Jobsbridge

Hadoop Technical Manager

Sunnyvale, CA · On-site

Company Description Jobsbridge 10+ years of proven experience in distributed systems, parallel computing, data warehousing and analytics. 6+ years of experience in large scale hadoop compute clusters ...

Brilliant

Audio Processing Engineer

San Mateo, CA · On-site

... compatible parallel computing frameworks to optimize audio processing performance, and an understanding of acoustics applied toward speaker and cavity design. Additional Information All your ...

Brilliant

Audio Processing Engineer

San Mateo, CA · On-site

... parallel computing frameworks to optimize audio processing performance, and an understanding of acoustics applied toward speaker and cavity design. Qualifications Additional Information All your ...

Booz Allen Hamilton

Mid HPC Engineer

Beavercreek, OH · On-site

$61K - $141K/yr

Experience with parallel computing concepts such as MPI and OpenMP, in a professional or academic environment * Experience with parallel file systems such as Lustre and GPFS or high-throughput ...

Nvidia

OR · On-site

$122K - $161K/yr

NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited ...

Brilliant

Speech Recognition Lead

San Mateo, CA · On-site

$20.50 - $25.75/hr

... parallel computing frameworks. Qualifications Additional Information All your information will be kept confidential according to EEO guidelines.

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Parallel Computing information

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$25K

$52.4K

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How much do parallel computing jobs pay per year?

As of Jul 11, 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.

Is parallel computing difficult?

Parallel computing as a job involves designing and implementing systems that perform multiple tasks simultaneously, which requires strong problem-solving skills, knowledge of algorithms, and proficiency with programming tools like MPI or OpenMP. The difficulty depends on the complexity of projects and the individual's experience, but mastering parallel algorithms and debugging concurrent processes can be challenging for beginners. Continuous learning and practical experience are essential for success in this field.

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 is the highest paying job in computing?

In computing, roles such as Chief Technology Officer (CTO), Solutions Architect, and Data Science Director tend to be among the highest paying, often earning six-figure salaries. Specialized skills in areas like artificial intelligence, cybersecurity, and cloud computing can also command top compensation levels for experienced professionals.

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.

What engineers make $500,000?

Senior engineers in fields such as software, aerospace, or petroleum engineering can earn $500,000 or more annually, often through a combination of base salary, bonuses, and stock options. High compensation typically requires extensive experience, advanced skills, and working in high-demand industries or leadership roles.

What is an example of parallel computing in real life?

Parallel computing in a job context involves tasks like processing large datasets or simulations simultaneously across multiple processors or cores to improve efficiency. For example, data analysts may use parallel computing tools to analyze big data sets quickly, requiring knowledge of programming languages such as Python or C++ and familiarity with parallel processing frameworks like MPI or OpenMP.
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What states have the most Parallel Computing jobs? States with the most job openings for Parallel Computing jobs include:
What job categories do people searching Parallel Computing jobs look for? The top searched job categories for Parallel Computing jobs are:
Parallel Computing jobs near you
Senior GPU System Architect

Senior GPU System Architect

Nvidia

Santa Clara, CA • Hybrid

Full-time

Re-posted 4 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

NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.

We are seeking a GPU System Architect who will architect and design multi-GPU scale-up and scale-out systems for next-generation datacenter platforms for AI and HPC. The architect in this role will explore and define system architectures that tightly couple GPU compute, high-bandwidth memory, in-package interconnects and GPU-to-GPU communication fabric subsystems to deliver industry-leading AI performance, scalability and resilience. The ideal candidate combines deep hands-on system-level fabric/networking architecture experience, and practical hardware-software co-design expertise.

What you will be doing:

  • Architect multi-GPU system topologies for scale-up and scale-out configurations, balancing AI throughput, scalability, and resilience.

  • Define, modify and evaluate future architectures for high-speed interconnects such as NVLink and Ethernet co-designed with the GPU memory system.

  • Collaborate with other teams to architect RDMA-capable hardware and define transport layer optimizations for GPU-based large scale AI workload deployments.

  • Use and modify system models, perform simulations and bottleneck analyses to guide design trade-offs.

  • Work with GPU ASIC, compiler, library and software stack teams to enable efficient hardware-software co-design across compute, memory, and communication layers.

  • Contribute to interposer, package, PCB and switch co-design for novel high-density multi-die, multi-package, multi-node rack-scale systems consisting of hundreds of GPUs.

What we need to see:

  • BS/MS/PhD in Electrical Engineering, Computer Engineering, or equivalent experience.

  • 8 years or more of relevant experience in system design and/or ASIC/SoC architecture for GPU, CPU or networking products.

  • Deep understanding of communication interconnect protocols such as NVLink, Ethernet, InfiniBand, CXL and PCIe.

  • Experience with RDMA/RoCE or InfiniBand transport offload architectures.

  • Proven ability to architect multi-GPU/multi-CPU topologies, with awareness of bandwidth scaling, NUMA, memory models, coherency and resilience.

  • Experience with hardware-software interaction, drivers and runtimes, and performance tuning for modern distributed computing systems.

  • Strong analytical and system modeling skills (Python, SystemC, or similar).

  • Excellent cross-functional collaboration skills with silicon, packaging, board, and software teams.

Ways to stand out from the crowd:

  • Background in system design for AI and HPC.

  • Experience with NICs or DPU architecture and other transport offload engines.

  • Expertise in chiplet interconnect architectures or multi-node fabrics and protocols for distributed computing.

  • Hands-on experience with interposer or 2.5D/3D package co-design.

#LI-Hybrid

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 May 11, 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

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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