This work is performed in highly regulated environments and demands deep expertise across systems ... Apply AI and machine-learning tools to accelerate circuit design, automate analysis, and improve ...
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This work is performed in highly regulated environments and demands deep expertise across systems ... Apply AI and machine-learning tools to accelerate circuit design, automate analysis, and improve ...
Principal Electrical Engineer
Phoenix, AZ · On-site
$130K - $159K/yr
Deep knowledge of high-speed digital design, signal-integrity fundamentals, IC package stack ups ... Experience with FPGA-based designs using Intel (Altera) or AMD (Xilinx) * Experience working with ...
Principal Electrical Engineer
Phoenix, AZ · On-site
$130K - $159K/yr
Deep knowledge of high-speed digital design, signal-integrity fundamentals, IC package stack ups ... Experience with FPGA-based designs using Intel (Altera) or AMD (Xilinx) * Experience working with ...
Fpga Deep Learning information
What is an FPGA Deep Learning engineer?
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 are the key skills and qualifications needed to thrive as an FPGA Deep Learning engineer?
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.
What is the difference between Fpga Deep Learning vs Machine Learning Engineer?
| Aspect | Fpga Deep Learning | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Bachelor's or higher in CS, EE, or related; knowledge of FPGA programming and deep learning frameworks | Bachelor's or higher in CS, Data Science, or related; expertise in ML algorithms and software development |
| Work Environment | Hardware-focused, embedded systems, FPGA development labs | Software-focused, data centers, cloud platforms, or research labs |
| Industry Usage | Embedded AI, edge computing, specialized hardware acceleration | Data 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 popular job titles related to Fpga Deep Learning jobs in Arizona?
For Fpga Deep Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Fpga Deep Learning jobs in Arizona look for?
The top searched job categories for Fpga Deep Learning jobs in Arizona are:
Specialist, Electrical Engineer (Transformation)
Phoenix, AZ • On-site
Acron Aviation
Full-time
Re-posted 20 days ago
Job description
About Acron Aviation (Avionics – Surveillance):
Acron Aviation designs, engineers, and manufactures certified avionics systems that support safety-critical flight operations across commercial and military aviation. Within Avionics, the Surveillance product line develops and sustains systems that enable aircraft awareness, collision avoidance, and airspace interoperability. Our portfolio includes TCAS, Mode S transponders, ADS-B, and integrated surveillance solutions deployed across commercial, military, and rotorcraft platforms. This work is performed in highly regulated environments and demands deep expertise across systems, hardware, software, and RF throughout the full product lifecycle.
Position Summary
As a Specialist Electrical Engineer within our Surveillance product line, you will drive innovation in the development of TCAS and transponder radios, advancing the electrical engineering discipline through concept generation, design, analysis, qualification, and lifecycle management.
You will apply rigorous data-driven analysis and modern AI-assisted engineering tools to accelerate design exploration, optimize hardware performance, and strengthen verification of avionics hardware. This role works within multidisciplinary project teams including mechanical, electrical (digital and analog), RF, firmware, and software engineering to plan, design, prototype, document, and test avionics transponders, RF systems, and sensor-based products.
Design activities will support surveillance avionics solutions for Air Transport, General Aviation, helicopters, and emerging platforms including Urban Air Mobility (UAM) and eVTOL aircraft.
Key Responsibilities - Conceptualize, design, integrate, and qualify digital, analog, and power circuitry, with supplemental RF principles, for avionics applications
- Design circuits utilizing microprocessors, memory devices, Ethernet, SPI, I2C, PCIe, and other avionics interfaces
- Develop and review electrical schematics and support PCB layout activities
- Support CAD layout reviews, preferably using Altium-based tools
- Generate engineering documentation including HRDs, HDDs, and HCIAs
- Provide technical inputs supporting certification documentation, including PHAC development
- Perform electrical design activities ranging from individual circuit design through aircraft-level system architectures
- Utilize electrical analysis tools for power distribution, stress analysis, and related simulations
- Apply AI and machine-learning tools to accelerate circuit design, automate analysis, and improve design verification and defect detection
- Evaluate and adopt emerging engineering tools, methods, and technologies that advance avionics hardware development
- Conduct trade studies and quantitative engineering analysis to optimize performance, cost, reliability, and manufacturability
- Support proposal efforts by estimating engineering tasks, identifying technical risks, and planning development activities
- Collaborate effectively across multiple engineering disciplines and technical teams
- Interface directly with customers to understand requirements and develop practical engineering solutions
- Perform other duties as assigned
- Travel up to 10%
- Bachelor’s Degree in Electrical Engineering or Computer Engineering and a minimum of 5 years of relevant experience; or Graduate Degree and a minimum of 4 years of related experience
- Minimum 2 years of experience with electrical hardware design; aerospace experience preferred
- Hands-on experience designing and developing avionics products, RF systems, or sensor-based products
- Demonstrated experience applying engineering analysis and modern software or AI-enabled tools to solve hardware design challenges
- Working knowledge of at least one programming language such as Python or C++
- Experience developing integrated aerospace hardware and software products
- Exposure to Model-Based Systems Engineering (MBSE)
- Experience applying AI or machine learning tools to hardware design, analysis, test automation, or engineering productivity initiatives
- Proficiency with data analysis and scripting tools, including Python, to automate workflows and support engineering decisions
- Understanding of FPGA and firmware development requirements and their impact on hardware design
- Experience with RTOS integration and associated hardware design considerations
- Practical understanding of RF design principles, particularly within the L-band frequency range
- Strong interpersonal skills with experience working within multidisciplinary engineering teams
- Excellent written, verbal, and presentation communication skills
- Knowledge of DO-160, DO-178, and DO-254
- Experience with Design-to-Cost and Design-for-Manufacturability methodologies
- Experience supporting production and test activities across geographically distributed teams
- Demonstrated ability to manage multiple programs and responsibilities simultaneously
- Ability to operate independently and determine appropriate paths forward with minimal oversight
- Pilot License or Part 107 Certificate
Equal Opportunity Statement
Acron Aviation is proud to be an Affirmative Action and Equal Opportunity Employer. We are committed to treating all employees and applicants with respect and dignity and to maintaining a workplace free from unlawful discrimination. All applicants will be considered for employment regardless of race, color, religion, age, national origin, ancestry, ethnicity, gender (including pregnancy, childbirth, breastfeeding, or other related medical conditions), gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, citizenship status, or any other legally protected characteristic.
Additional Information:
Acron Aviation maintains a drug-free workplace and conducts pre-employment substance abuse testing and background checks, where allowed by law. Acron Aviation is an E-Verify employer.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.