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Parallel Computing Jobs in Virginia (NOW HIRING)

Leidos

GPU Software Engineer

Arlington, VA · On-site

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Arlington, VA · On-site

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Arlington, VA · On-site

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Cymertek

HPC Cloud Performance Engineer

Chantilly, VA · On-site

$57.50 - $77/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Cymertek

HPC Cloud Performance Engineer

Reston, VA · On-site

$58 - $77.75/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Cymertek

HPC Cloud Performance Engineer

Tysons, VA · On-site

$56 - $74.75/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Booz Allen Hamilton

Embedded FPGA Engineer

Mclean, VA · On-site

$86K - $198K/yr

Experience with GPU acceleration, parallel computing, or high‑bandwidth data processing. * Experience gathering requirements, shaping development roadmaps, or supporting Agile workflows.

Cymertek Corporation

HPC Software Engineer

Reston, VA · On-site

Responsibilities : • Design and develop software for high-performance computing systems • Optimize applications for scalability and efficiency on HPC architectures • Implement parallel ...

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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 Sep 13, 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 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.

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 August 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 100% In-person job distribution, with an average salary of $51,911 per year, or $25 per hour.

Data Scientist - Tech - Top Secret required to apply - DC area

Reston, VA

Bow Wave LLC
See more jobs at Bow Wave LLCexternal

Full-time

Re-posted 11 days ago

Job description

Conducts data analytics, data engineering, data mining, exploratory analysis, predictive

analysis, and statistical analysis, and uses scientific techniques to correlate data into graphical,

written, visual and verbal narrative products, enabling more informed analytic decisions.

Proactively retrieves information from various sources, analyzes it for better understanding about

the data set, and builds AI tools that automate certain processes. Duties typically include:

creating various ML-based tools or processes, such as recommendation engines or automated

lead scoring systems. Performs statistical analysis, applies data mining techniques, and builds

high quality prediction systems. Should be skilled in data visualization and use of graphical

applications, including Microsoft Office (Power BI) and Tableau; major data science languages,

such as R and Python; managing and merging of disparate data sources, preferably through R,

Python, or SQL; statistical analysis; and data mining algorithms. Should have prior experience

with large data Multi-INT analytics, ML, and automated predictive analytics.

Contractor shall:

• Create data packages, in the form of databases, reports, and visualization'

• Communicate ongoing data science activities, technical findings, and data products for both

technical and non-technical customers

• Extract relevant features from large data stores containing open source, PIA, and CAI,

containing bad records, partial records, errors, or other forms of "noising."

• Extract features from open source information stored in a wide range of possible formats,

including JSON, XML, raw text logs, industry-specific encodings, and graph link data;

• Apply natural language processing, computer vision, signal processing, and speaker and speech

recognition algorithms to identify objects in text, image, video, and audio files;

• Apply descriptive and inferential statistics to describe data and make

predictions about the data, including statistical tests to determine confidence for a hypothesis,

common summary statistics (e.g. mean, variance, and counts), fit distributions to datasets and

use those distributions to predict event likelihoods;

• Be able to execute data science method using parallel computing

frameworks (e.g. deepleaming4j, Torch, Tensor Flow, Caffe, Neon, NVIOFFICE CUDA Deep

Neural Network library (cuDNN), and OpenCV)) and distributed data processing frameworks

( e.g. Hadoop (including HDFS, Hbase, Hive, Impala, Giraph, Sqoop ), Spark (inlcuding MLib,

GraphX, SQL and Dataframes)

• Be able to execute data science method using common programming/scripting

languages: Python, Java, Scala, R (statistics).

Apply