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Parallel Computing Jobs in Washington (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 ...

Leidos

GPU Software Engineer

Arlington, VA

$69K - $125K/yr

An understanding of GPU programming and parallel computing architectures * Grow and develop experience in: * Signal processing algorithms written in MATLAB * Parallelization of existing algorithms

Leidos

GPU Software Engineer

Arlington, VA · On-site

$69K - $125K/yr

An understanding of GPU programming and parallel computing architectures * Grow and develop experience in: * Signal processing algorithms written in MATLAB * Parallelization of existing algorithms

Leidos

GPU Software Engineer

Arlington, VA · On-site

$69K - $125K/yr

An understanding of GPU programming and parallel computing architectures * Grow and develop experience in: * Signal processing algorithms written in MATLAB * Parallelization of existing algorithms

Unity Compass

FSR Engineer

Washington, DC · On-site

Oracle, PostgreSQL, Cassandra, Hadoop, and Spark for distributed data storage and parallel computing. * Back-End Development: Java for backend applications and data integration tools. * Front-End ...

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Parallel Computing information

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.
What are popular job titles related to Parallel Computing jobs in Washington? For Parallel Computing jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Parallel Computing jobs in Washington look for? The top searched job categories for Parallel Computing jobs in Washington are:
Parallel Computing jobs near you

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

Bow Wave LLC

Reston, VA • On-site

$165K - $175K/yr

Full-time

Posted 10 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