Computer Vision Fpga information
See salary details
$48.5K - $56.6K
0% of jobs
$56.6K - $64.7K
0% of jobs
$64.7K - $72.8K
7% of jobs
$72.8K - $80.9K
0% of jobs
$109.9K is the 25th percentile. Wages below this are outliers.
$105.1K - $113.2K
28% of jobs
$113.2K - $121.3K
1% of jobs
$121.3K - $129.4K
0% of jobs
The median wage is $131K / yr.
$129.4K - $137.5K
62% of jobs
How much do computer vision fpga jobs pay per year?
As of Sep 9, 2026, the average yearly pay for computer vision fpga in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.
A Computer Vision FPGA is a field-programmable gate array (FPGA) that is specifically designed or programmed to accelerate computer vision tasks such as image processing, object detection, and pattern recognition. FPGAs are integrated circuits that can be reconfigured for specific computing tasks, making them well-suited for the real-time, high-speed processing demands of computer vision applications. Using FPGAs for computer vision enables lower latency, high throughput, and energy efficiency compared to traditional CPUs or GPUs for certain tasks. This combination is particularly valuable in fields like robotics, autonomous vehicles, and industrial automation.
To thrive as a Computer Vision FPGA Engineer, you need strong expertise in digital design, VHDL/Verilog programming, and a solid background in computer vision algorithms, often supported by a degree in electrical engineering or computer science. Familiarity with FPGA development tools such as Xilinx Vivado or Intel Quartus, and experience with simulation and debugging systems, are typically required. Critical soft skills include problem-solving, attention to detail, and effective teamwork to tackle complex design and integration challenges. These skills ensure efficient implementation of high-performance vision systems and successful collaboration in interdisciplinary engineering environments.
Computer Vision FPGA engineers often encounter challenges related to balancing processing speed, resource utilization, and power consumption when implementing algorithms on hardware. Translating complex computer vision algorithms to run efficiently on FPGAs requires a deep understanding of hardware constraints and the ability to optimize code for parallel processing. Additionally, these engineers frequently collaborate with software developers, data scientists, and hardware teams to adapt algorithms for real-time performance while ensuring accuracy and scalability, which can involve extensive iteration and testing.
What other helpful pages are available for Computer Vision Fpga?
Other pages related to Computer Vision Fpga:
