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No Experience Computer Engineering Jobs in Washington

Bespoke Technologies, Inc

R&D Computer Engineer

Washington, DC · On-site

$126K - $148K/yr

... Engineering or Computer Engineering to join our R&D team as an R&D Computer Engineer. This role offers a unique opportunity to gain hands-on research experience at the intersection of embedded ...

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No Experience Computer Engineering information

What entry-level tasks can I expect as a computer engineering professional with no prior experience?

As a computer engineering professional starting out with no experience, you will likely begin with foundational tasks such as assisting in hardware testing, debugging basic software, documenting code, and supporting senior engineers with ongoing projects. You may also be involved in assembling prototypes, running performance simulations, and participating in team meetings to learn about project requirements and workflows. These responsibilities are designed to help you build technical skills, gain familiarity with industry tools, and understand team collaboration, all of which are essential for career growth in computer engineering.

What are the key skills and qualifications needed to thrive as a Computer Engineer with no prior experience, and why are they important?

To thrive as a Computer Engineer with no prior experience, you typically need a foundational understanding of programming, computer systems, and basic engineering principles, often gained through a relevant degree or coursework. Familiarity with languages like Python or C++, as well as exposure to development environments and version control systems like Git, is valuable. Strong problem-solving skills, willingness to learn, and effective communication help newcomers adapt quickly and collaborate with more experienced team members. These skills and qualities are important because they enable entry-level engineers to contribute, grow, and build a successful career in a technology-driven field.

What is the difference between No Experience Computer Engineering vs Computer Support Specialist?

AspectNo Experience Computer EngineeringComputer Support Specialist
Required CredentialsNone or basic certifications (e.g., CompTIA A+)Basic certifications often preferred, such as CompTIA A+
Work EnvironmentLabs, offices, or remote; focus on hardware/software setupHelp desks, client sites, or remote; troubleshooting and support
Industry UsageEntry-level roles in tech companies, startups, or educational institutionsIT support, customer service, and technical assistance roles
Search & Comparison IntentUnderstanding entry points into computer engineering without experienceEntry-level support roles for those with minimal experience

While No Experience Computer Engineering focuses on foundational skills and learning in hardware and software design, Computer Support Specialists primarily troubleshoot and assist users with existing systems. Both roles often require similar certifications and work environments, but they serve different career paths within the tech industry.

What are the most commonly searched types of Computer Engineering jobs in Washington? The most popular types of Computer Engineering jobs in Washington are:
No Experience Computer Engineering jobs near you
Infographic showing various No Experience Computer Engineering job openings in Washington as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 79% Physical, 4% Hybrid, and 17% Remote job distribution.
R&D Computer Engineer

R&D Computer Engineer

Bespoke Technologies, Inc

Washington, DC • On-site

$126K - $148K/yr

Full-time

Posted 6 days ago

New

Job description

BT-344 - R&D Computer Engineer (Graduate Student Research Position)
Location: DMV, Remote-Hybrid
** Must be able to obtain a clearance if offered the position**
Position Overview
We are seeking highly motivated graduate students in Electrical Engineering or Computer Engineering to join our R&D team as an R&D Computer Engineer. This role offers a unique opportunity to gain hands-on research experience at the intersection of embedded systems, machine learning, and audio technologies. The successful engineer will contribute to the design, development, optimization, and evaluation of machine learning solutions deployed on resource-constrained embedded platforms.
Responsibilities
  • Design and develop software in C++ for embedded systems applications.
  • Implement and optimize machine learning algorithms on microcontrollers and neural accelerator hardware.
  • Conduct system-level testing, validation, and performance benchmarking.
  • Perform power profiling and energy-efficiency analysis of embedded AI workloads.
  • Develop and maintain technical documentation, reports, and research artifacts.
  • Collaborate with engineers and researchers to evaluate new architectures, algorithms, and deployment strategies.
  • Support prototyping, debugging, and integration activities across hardware and software platforms.
  • Required Experience/Skills
  • Strong C++ software development experience.
  • Embedded systems development experience.
  • Experience with microcontrollers.
  • Knowledge of neural accelerators.
  • Machine learning fundamentals and implementation (e.g., CNNs).
  • Power profiling and performance analysis.
  • Software and system testing methodologies.
  • Technical documentation and reporting.
  • Preferred / Nice-to-Have Skills
  • Audio signal processing.
  • TinyML model training and deployment.
  • Anomaly detection methods.
  • Gaussian mixture models.
  • Education Requirements
  • Cumulative and major GPA above 3.5.
  • Currently pursuing a degree in Electrical Engineering or Computer Engineering.
  • Exceptional upperclassmen (seniors) may be considered on a case-by-case basis.
  • Preferred Qualifications
  • Bachelor's degree in Electrical Engineering or Computer Engineering from an ABET-accredited program.
  • Currently pursuing a Master's or Ph.D. in Electrical Engineering or Computer Engineering.
  • What You'll Gain
  • Hands-on research experience in embedded systems and machine learning.
  • Exposure to cutting-edge neural accelerator technologies.
  • Experience deploying AI solutions on resource-constrained hardware.
  • Opportunities to contribute to innovative audio-focused research projects.
  • Flexible remote work environment and collaboration with experienced researchers and engineers.

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