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Parallel Computing Jobs in Virginia (NOW HIRING)

SAIC

HPC Systems Engineer

Charlottesville, VA

Distributed and Containerized Workloads Experience supporting distributed compute workloads utilizing parallel computing frameworks such as: * MPI * OpenMP * GPU compute frameworks Candidates should ...

SAIC

HPC Systems Engineer

Charlottesville, VA

Distributed and Containerized Workloads Experience supporting distributed compute workloads utilizing parallel computing frameworks such as: * MPI * OpenMP * GPU compute frameworks Candidates should ...

SAIC

HPC Systems Engineer

Charlottesville, VA · On-site

Distributed and Containerized Workloads Experience supporting distributed compute workloads utilizing parallel computing frameworks such as: * MPI * OpenMP * GPU compute frameworks Candidates should ...

teKnoluxion

HPC Support Engineer

Charlottesville, VA · On-site

Experience with parallel computing frameworks (MPI, OpenMP, or CUDA-based GPU workloads) * Experience supporting scientific or engineering applications requiring large-scale compute resources

teKnoluxion

HPC Systems Engineer

Charlottesville, VA · On-site

Experience with parallel computing frameworks (MPI, OpenMP) * Experience with configuration management tools (Ansible, Puppet) * Experience supporting GPU-enabled environments and CUDA workloads

teKnoluxion

HPC Systems Engineer

Charlottesville, VA · On-site

Experience with parallel computing frameworks (MPI, OpenMP) * Experience with configuration management tools (Ansible, Puppet) * Experience supporting GPU-enabled environments and CUDA workloads

SAIC

HPC Support Engineer

Charlottesville, VA · On-site

Distributed Compute Workloads Experience supporting distributed workloads utilizing parallel computing frameworks such as: * MPI * OpenMP Experience supporting the compilation and execution of ...

SAIC

HPC Support Engineer

Charlottesville, VA

Distributed Compute Workloads Experience supporting distributed workloads utilizing parallel computing frameworks such as: * MPI * OpenMP Experience supporting the compilation and execution of ...

SAIC

HPC Support Engineer

Charlottesville, VA

Distributed Compute Workloads Experience supporting distributed workloads utilizing parallel computing frameworks such as: * MPI * OpenMP Experience supporting the compilation and execution of ...

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Parallel Computing information

See Virginia salary details

$24.8K

$51.9K

$89.7K

How much do parallel computing jobs pay per year?

As of May 31, 2026, the average yearly pay for parallel computing in Virginia is $51,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,700.00 and $59,000.00 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 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 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 are popular job titles related to Parallel Computing jobs in Virginia? For Parallel Computing jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Parallel Computing jobs in Virginia look for? The top searched job categories for Parallel Computing jobs in Virginia are:
Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in Virginia as of May 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $51,911 per year, or $25 per hour.

Staff Software Engineer, AI/ML, Google Public Sector

Google

Reston, VA • On-site

Full-time

Posted 2 days ago

Google rating

8.8

Company rating: 8.8 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

30th of 184 rated software companies

Job description

Job Summary:
Google is a leading technology company seeking a technical Senior AI/ML Software Engineer to lead the architecture and deployment of large-scale distributed data systems and advanced machine learning pipelines. The role involves designing infrastructure for analyzing high-throughput data streams and optimizing workloads for specialized hardware accelerators while providing mentorship to engineering teams.
Responsibilities:
• Architect and operate advanced data synthesis pipelines and AI-based retrieval applications.
• Manage petabyte-scale data ingestion and synchronization across compute environments, including local storage, cloud backends, and on–prem resources.
• Optimize highly parallel numerical operations and ML inference algorithms for specialized hardware accelerators.
• Lead technical direction and provide engineering mentorship for groups developing complex production software systems.
• Implement rigorous data life-cycle policies to ensure system resilience, data integrity, and fault recovery at scale.
Qualifications:
Required:
• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C++, Java, Python, Kotlin or Go.
• Experience in technical leadership, including defining technical road maps, delivering projects, and maintaining code quality standards.
• Experience in parallel computing paradigms, hardware-level optimization, and low-level accelerator optimization.
Preferred:
• Master's degree or PhD in a quantitative discipline (e.g., Computer Science, Physics, Applied Mathematics, or similar).
• 8 years of experience designing, building, and operating large-scale distributed data systems and production machine learning deployments.
• Experience deploying modern deep learning architectures using frameworks like PyTorch or TensorFlow on large-scale clusters.
• Experience with cloud-native infrastructure (Docker, Kubernetes) and managing distributed filesystems and cloud object storage.
• Active, or the ability to obtain, a Secret security clearance.
Company:
Google specializes in internet-related services and products, including search, advertising, and software. It is a sub-organization of Alphabet. Founded in 1998, the company is headquartered in Mountain View, USA, with a team of 10001+ employees. The company is currently Late Stage.

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