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Fpga Deep Learning Jobs (NOW HIRING)

Altera

FPGA System Design Engineer

San Jose, CA · On-site

$118K - $172K/yr

Artificial Intelligence and Deep Learning * High-speed ADC/DAC architectures * RF and Software Defined Radio (SDR) systems * Experience with FPGA SoCs, embedded processors, and heterogeneous ...

Citadel Securities

FPGA Engineer - Intern (US)

Miami, FL

$112.50 - $145/hr

A deep passion for technology and hardware development * Pursuing a Bachelor's, Master's or PhD in ... learning and AI to power our analytics and tackle the market's and our clients' most critical ...

Citadel Securities

FPGA Engineer - Intern (US)

Miami, FL · On-site

$4.5K - $5.8K/wk

A deep passion for technology and hardware development * Pursuing a Bachelor's, Master's or PhD in ... learning and AI to power our analytics and tackle the market's and our clients' most critical ...

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Fpga Deep Learning information

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$70K

$147.1K

$210.5K

How much do fpga deep learning jobs pay per year?

As of Aug 30, 2026, the average yearly pay for fpga deep learning in the United States is $147,056.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,000.00 and $169,500.00 per year, depending on experience, location, and employer.

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?

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.

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Fpga Deep Learning jobs near you
Infographic showing various Fpga Deep Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $147,056 per year, or $70.7 per hour.

Senior FPGA Prototyping Platform Engineer

Santa Clara, CA

Nvidia
Computer and Electronic Product Manufacturing • 10K+ employees

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

7th of 246 rated software companies

Great coworkers

People enjoy working here

Good employer

Learn more about Nvidiaexternal

$144K - $199K/yr

Full-time

Re-posted 28 days ago

Job description

Are you passionate about FPGA prototyping. Are you interested in pushing the boundaries of innovation to make FPGA prototypes faster and more efficient. Can you work in a fast-paced environment that requires coordination between many teams across geographies and resolving sophisticated problems daily.

If so, we are looking for hardworking engineers who will craft FPGA prototypes of our next generation GPUs and SOCs on standard FPGA prototyping platforms. We are now looking for a Senior FPGA Prototyping Platform Engineer to join our Emulation team onsite in Santa Clara, CA. What you'll be doing: Build FPGA prototypes by taking the design through synthesis and place and route, analyzing timing and generating bitstreams.

Bring up the design on FPGA prototyping platforms involving hands-on lab debug with HW and SW debuggers. Release the prototype to the customers and support them when they face problems. Using HW diagnostics specific to Prototyping platforms to help isolate issues between HW platform, FPGA design programmed on to platform.

Good coordination with verification engineers and SW teams and Lab Technicians will be needed to accomplish your tasks. What we need to see: BS (or equivalent experience) in Electrical Engineering, Computer Engineering, or related fields with 7+ years of experience in FPGA prototyping, or MS with 5+ years of proven experience in FPGA prototyping. Good understanding of FPGA prototyping hardware architecture, flows and tools.

Experience with hands-on FPGA prototyping lab tasks and working with or managing lab technicians in lab environment of medium to large scale. Experience in backend flows of FPGA Prototyping - Synthesis, P&R and Timing closure, with emphasis on Synopsys Protocompiler and Xilinx Vivado. Hands-on experience with lab FPGA debug methodologies, tools (Identify and/or ChipScope), and lab debug equipment (oscilloscopes, logic/protocol analyzers).

Ways to stand out from the crowd: Experience with carrying out independent as well as collaborative debug tasks and ability to quickly triage and root-cause issues is a major plus. Prior experience with prototyping (Synopsys HAPS or FPGA platforms) of a high-performance processor or SOC is a plus. NVIDIA has continuously reinvented itself over two decades.

Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world.

This is our life's work, to amplify human imagination and intelligence. Make the choice to join us today. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until June 6, 2026.

This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.

As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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