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Parallel Computing Jobs (NOW HIRING)

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)

Jobgether

$70 - $100/hr

You'll investigate potential memory allocation issues, execution bottlenecks, and parallel computing logic errors. Your feedback will help improve the quality, reliability, and technical rigor of AI ...

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 ...

Cymertek

HPC Software Architect

Honolulu, HI · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

Aurora, CO · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek Corporation

HPC Software Architect

Reston, VA · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

Annapolis Junction, MD · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

Tysons, VA · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

Reston, VA · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

Chantilly, VA · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek

HPC Software Architect

San Antonio, TX · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek Corporation

HPC Software Architect

Aurora, CO · Hybrid

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

Cymertek Corporation

HPC Software Architect

Aurora, CO · On-site

The ideal candidate has a deep understanding of parallel computing, HPC frameworks, and system-level optimization, along with a passion for solving complex challenges in high-performance environments.

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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 Sep 13, 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 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 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 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 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.

Is parallel computing hard?

Parallel computing as a job involves designing and managing systems that perform multiple calculations 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 tasks and the level of expertise, but it generally involves understanding concurrency, synchronization, and performance optimization. Gaining experience through coursework, certifications, and hands-on projects can help reduce the learning curve.
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Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution, with an average salary of $52,360 per year, or $25.2 per hour.

PyTorch Role/C++,Python

Bellevue, WA • On-site

Staffingine LLC
Recruiting and Staffing Services • 51 - 200 employees
Learn more about Staffingine LLCexternal

Full-time

Re-posted 3 days ago

Job description

Job Title: PyTorch Role/C++,Python Role
Job Location: Bellevue, WA
Job Type: Full time

Job Description:

  1. 10+ years of experience in systems/software engineering or HPC/AI development  
  2. Strong programming expertise in Python and low-level programming (C/C++ preferred)
  3. Deep understanding of:
  4. GPU accelerator architectures
  5. Parallel computing models
  6. Memory and compute optimization
  7. Experience with:
  8. PyTorch (model-level workloads and execution)
  9. Triton (custom kernel development / compiler-level interaction)
  10. Distributed computing frameworks (MPI, NCCL, etc.)
  11. Strong knowledge of:
  12. Compiler stacks / SDK architecture
  13. Runtime systems and execution pipelines