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Fpga Deep Learning Jobs in Raleigh, NC (NOW HIRING)

... deep learning, machine learning, autonomous system control and decision systems, computer vision ... Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ...

... deep learning, machine learning, autonomous system control and decision systems, computer vision ... Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ...

... deep learning, machine learning, autonomous system control and decision systems, computer vision ... Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ...

Electronic Warfare Systems Architect

Raleigh, NC · On-site

$217K/yr

Emitter detection, classification, and geolocation - Guide the integration of machine learning ... Deep expertise in radar signal processing, including detection, estimation, tracking, and ...

Fpga Deep Learning information

See Raleigh, NC salary details

$68K

$142.9K

$204.6K

How much do fpga deep learning jobs pay per year?

As of Jun 19, 2026, the average yearly pay for fpga deep learning in Raleigh, NC is $142,950.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,600.00 and $164,800.00 per year, depending on experience, location, and employer.

What are FPGA Deep Learning engineers?

FPGA Deep Learning engineers are professionals who design, implement, and optimize deep learning models to run efficiently on Field-Programmable Gate Arrays (FPGAs). FPGAs are specialized hardware chips that can be programmed to perform specific computational tasks at high speeds and low power consumption. These engineers bridge the gap between artificial intelligence algorithms and hardware, ensuring that neural networks and AI applications can leverage FPGA advantages such as parallelism and flexibility. Their work is crucial in industries requiring real-time data processing, like autonomous vehicles, robotics, and edge computing.

What is the difference between Fpga Deep Learning vs Machine Learning Engineer?

AspectFpga Deep LearningMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, EE, or related; knowledge of FPGA programming and deep learning frameworksBachelor's or higher in CS, Data Science, or related; expertise in ML algorithms and software development
Work EnvironmentHardware-focused, embedded systems, FPGA development labsSoftware-focused, data centers, cloud platforms, or research labs
Industry UsageEmbedded AI, edge computing, specialized hardware accelerationData analysis, predictive modeling, software solutions across industries

While both roles involve AI and machine learning, Fpga Deep Learning specialists focus on hardware acceleration using FPGAs to optimize deep learning models, whereas Machine Learning Engineers develop and deploy ML algorithms primarily in software environments. The roles often overlap in AI projects but differ in technical focus and work environment.

What are the key skills and qualifications needed to thrive as an FPGA Deep Learning Engineer, and why are they important?

To thrive as an FPGA Deep Learning Engineer, you need a solid background in digital design, hardware description languages (such as VHDL or Verilog), deep learning frameworks, and a relevant degree in electrical engineering, computer engineering, or a similar field. Familiarity with FPGA development tools (like Xilinx Vivado or Intel Quartus), hardware accelerators, and experience with deploying neural networks on embedded systems are typically required. Problem-solving ability, attention to detail, and strong collaboration skills are key soft skills that make a candidate stand out. These skills and qualities are essential for efficiently bridging the gap between AI algorithms and hardware implementations, ensuring high-performance, reliable solutions.

How do professionals in FPGA Deep Learning roles typically collaborate with software and data science teams?

FPGA Deep Learning professionals often work closely with software engineers and data scientists to optimize deep learning models for hardware acceleration. This collaboration involves translating neural network architectures from high-level frameworks (like TensorFlow or PyTorch) into efficient hardware implementations, communicating constraints or opportunities for parallelization, and iteratively refining models for performance. Regular meetings and code reviews are common to ensure alignment between hardware and software development. Effective communication and understanding of both domains are essential for successfully deploying deep learning solutions on FPGA platforms.
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Other

Medical, Dental, PTO

Posted 12 days ago


Job description

Software Engineer

Teledyne Scientific Company (TSC) has a legacy of over 60 years of advanced research and technology development. Our customer base consists of DARPA, ONR, AFRL, ARL, and other government and proprietary customers. TSC's Intelligent Systems Laboratory in North Carolina is a state-of-the-art research and development research facility, dedicated to R&D in the areas of applied neuroscience, autonomous systems and artificial intelligence. The laboratory currently is host to world-class scientists and engineers, with advanced degrees in computer science, mathematics, electrical engineering, neuroscience, chemistry, biomedical engineering and mechanical engineering.

Teledyne Scientific Company is seeking an exceptional Software Engineer to support advanced research and development projects that require algorithm and software development for autonomy, machine learning and biomedical applications. This position is onsite, at our research laboratory in Research Triangle Park, Durham, North Carolina.

Primary Responsibilities:

Define, develop, and deliver novel solutions to a broad range of problems that include applications in target detection and tracking, deep learning, machine learning, autonomous system control and decision systems, computer vision, signal analysis, and brain computer interfaces, for the Government and commercial industry. Specific duties include:

  • In collaboration with other colleagues, lead development and implement advanced algorithms on PC, GPU and SoC (system on a chip) architectures (~50%).
  • Assist with software design, architecture, and implementation across a diverse set of projects that include: advanced research, mobile applications, platform technologies (~45%).
  • A small amount of travel (less than 5% time) is anticipated.

Qualifications & Competencies

Skills/Experience:

  • 5-10 years of experience
  • Experience with C++ is required
  • Experience with Docker is required
  • Experience with CMake is required
  • Familiarity with software development practices (revision control/compiler tricks/debugger, etc.), under Linux is required
  • Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking, storage systems, Unix/Linux, MATLAB, etc. is a plus
  • Excellent interpersonal and communication (presentation & writing) skills
  • Coursework in software development and computer architecture is required
  • Coursework in algorithms is strongly desired
  • Familiarity with automated testing is a plus

Education:

  • Candidates are required to have at least a B.S. in Computer Science, Applied Mathematics, Computer Engineering or Electrical Engineering

Citizenship & Security Clearance:

  • Candidates are required to be U.S. Citizens
  • Current SECRET clearance or minimally able to obtain Security Clearance

What can Teledyne offer YOU? A Competitive Salary & Benefits PackageExcellent Health, Dental, VisionPaid Vacation TimePaid Sick TimeLife Insurance BenefitsPaid Holidays401(k) EligibilityEmployee Stock Purchase PlanEducational Tuition ReimbursementEmployee Fun Events throughout the year

Teledyne is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age, or any other characteristic or non-merit based factor made unlawful by federal, state, or local laws.