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Computer Vision Engineer

A Computer Vision Engineer builds systems that interpret images and video. They develop models for detection, classification, segmentation, and tracking, and deploy them in products from cameras to robotics to medical imaging. The role pairs deep learning expertise with software engineering to turn visual data into reliable, real-time understanding.

Responsibilities

Build models for detection, segmentation, and tracking
Prepare and augment image and video datasets
Optimize models for accuracy and real-time performance
Deploy vision systems to cloud or edge devices
Evaluate models on vision-specific metrics
Collaborate with hardware and product teams

Must-have skills

Deep learning for computer vision (CNNs, transformers)
Python and frameworks such as PyTorch or TensorFlow
Image and video data processing
Model evaluation and optimization
Software engineering fundamentals

Nice-to-have skills

Edge deployment and model quantization
3D vision or SLAM
Experience with OpenCV
Domain expertise (medical, automotive, retail)

Average Salary

Typical US base salary for an HR Generalist by experience level.

Junior

0–2 yrs experience

$110,000 – $140,000
US annual salary (USD)

Intermediate

0–2 yrs experience

$140,000 – $180,000

US annual salary (USD)

Senior

0–2 yrs experience

$180,000 – $240,000

US annual salary (USD)

Figures are annual US market estimates for orientation, not offers. Actual pay varies by location, company stage, and equity.

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Frequently asked questions

What does a Computer Vision Engineer do?
They build models that understand images and video for tasks like object detection, classification, and tracking, then deploy them into real products.
What industries hire Computer Vision Engineers?
Automotive, robotics, healthcare, retail, security, agriculture, and manufacturing all rely heavily on computer vision.
What is edge deployment in computer vision?
Running vision models directly on devices such as cameras or phones, rather than in the cloud, which requires optimizing models to be small and fast.
Do I need a specialized degree?
A strong background in machine learning and vision helps, but hands-on project experience and solid engineering skills matter most.

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