Embedded Computer Vision information
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$79.5K - $88.9K
2% of jobs
$88.9K - $98.4K
3% of jobs
$98.4K - $107.8K
6% of jobs
$107.8K - $117.3K
5% of jobs
$117.3K - $126.7K
5% of jobs
$130K is the 25th percentile. Wages below this are outliers.
$126.7K - $136.2K
5% of jobs
$136.2K - $145.6K
7% of jobs
$145.6K - $155.1K
3% of jobs
$155.1K - $164.5K
3% of jobs
The median wage is $165.8K / yr.
$164.5K - $174K
58% of jobs
How much do embedded computer vision jobs pay per year?
As of Sep 9, 2026, the average yearly pay for embedded computer vision in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 per year, depending on experience, location, and employer.
Embedded computer vision refers to the integration of computer vision algorithms and techniques directly into hardware devices, such as cameras, smartphones, autonomous vehicles, or industrial machinery. Unlike traditional computer vision systems that require powerful external computers, embedded computer vision systems process visual data locally on devices with limited resources. This allows for real-time image and video analysis, lower latency, improved privacy, and reduced energy consumption, making them ideal for applications like robotics, surveillance, and IoT devices.
To thrive as an Embedded Computer Vision Engineer, you generally need strong programming skills in C/C++, a deep understanding of computer vision algorithms, and experience with embedded systems, often supported by a degree in computer engineering or a related field. Familiarity with tools and frameworks like OpenCV, TensorFlow Lite, and hardware platforms such as ARM Cortex or NVIDIA Jetson, as well as relevant certifications, is highly valuable. Critical soft skills include problem-solving, attention to detail, and effective communication for collaborating with multidisciplinary teams. These competencies ensure robust, efficient deployment of vision solutions on resource-constrained devices, driving innovation and product performance.
Professionals in embedded computer vision often encounter challenges such as limited processing power, memory constraints, and real-time performance requirements on edge devices. To address these, it's essential to optimize algorithms for efficiency, leverage hardware accelerators (like GPUs or dedicated vision processors), and carefully manage memory usage. Collaboration with hardware engineers and software developers is common to ensure that solutions are both performant and scalable. Staying updated with the latest advances in model compression and efficient neural network architectures is also critical for success in this role.
What other helpful pages are available for Embedded Computer Vision?
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