Computer Vision Engineer Salary and Jobs Guide

Sight is such an integral part of the human experience that most of us take it for granted.

It’s the primary system through which we take in information about the world around us. But it’s also the largest single system in the human brain—deciphering all that information takes a lot of horsepower. Millions of years of evolutionary craftiness are in play with every glance.

For computers to take on the reasoning and problem-solving abilities of humans, they will need access to the same kind of information about the world. Computer vision is the critical piece of that puzzle. But it’s a field that is also running into some of the hardest problems of perception and categorization of any aspect of artificial intelligence.

That’s why the job of computer vision engineering has become a dedicated role in the AI world. Computer vision engineers are in demand across a wide range of industries and applications. But the education and skillset required to land those positions can be formidable.

How Computer Vision Engineers Contribute to the Advancement of AI More Broadly

There’s little doubt that computer vision remains one of the hard problems in AI. Computer vision engineers are the people responsible for solving it.

Vision seems simple enough to most people. You look, you see, you interpret.

But each of those steps, implemented from scratch in a silicon-based system, represents a series of huge challenges:

Computer vision engineers are responsible for breaking down each one of those elementary steps into actions in the mind of the machine. It involves a command of:

Visual sensor systems and cameras, including an understanding of the physics of light waves
Image processing algorithms that can analyze a picture, detecting lines and curves that delineate distinct objects, understanding how light and color influence those perceptions, and labeling the objects correctly
Reasoning engines that can understand the meaning of those objects, the relationships between them, and make predictions about movement and behavior from the input

They put those systems together for uses as varied as document image scanning, autonomous robot navigation, and independent spacecraft operation.

How to Become a Computer Vision Engineer in AI

As important as the role of computer vision engineer may be in AI and in society, it’s not necessarily an easy job to get into.

The complexity of the system as a whole requires a heavy-duty education to master. So expect to earn at least a four-year degree and very likely a two-year master’s on top of it. Many of the leading computer vision engineers, directing big projects or tackling the hardest problems, have doctorates.

Education is only a starting point, however. Practical experience will put that knowledge to use in ways that potential employers will need to see. So building on research or class projects, it’s a good idea to get your hands dirty in actual experimentation in image processing. There are enough open-source tools and publicly available image sets that this isn’t a big obstacle for even people with modest coding skills.

Speaking of coding skills, you’ll need them. So investing time into building a foundation in programming concepts and techniques is important. You’ll want to look at the latest machine learning libraries and toolsets and get familiar with them. And since much of the processing in modern vision training happens in the cloud, learning about platforms like Azure and AWS is also a good idea.

Math and physics are constant elements in any computer vision system, so a mastery of those concepts is also a necessary step to computer vision engineering positions.

Finally, though optional, picking up additional skills in robotics and automation can be a good idea. Although not strictly necessary in every computer vision application, many companies using computer vision will be using it in such physical systems.

On top of that, many robotics projects offer practical ways to get experience in the uses for computer vision. They may also offer a gentler introduction, using off-the-shelf systems, so you can analyze and understand what’s happening. That’s a big leg up when it comes time to put together your own system from scratch.

What Is the Job Description for a Computer Vision Engineer?

Because computer vision has a lot of highly complex tributary fields, the specific tasks they perform can vary a lot from job to job. Those working on sensors will need skills in electrical engineering as well as electronics; others may never touch a camera but need a strong master of machine learning algorithm development and neural networks to break down images.

This can lead to various job titles for the position:

Computer Vision Algorithm Engineer
Machine Learning-Computer Vision Engineer
Computer Vision Engineer, Data Scientist
Principal Software Engineer, Digital Signal Processing
3D Computer Vision and Perception Engineer in Mapping

In most cases, computer vision engineers will be coordinating their efforts up or down the chain. They may work with other AI specialists as just one cog in a larger machine on a big project, like robotics development. They will also deal with specialists in camera systems and imaging.

In many ways, it’s a very standard kind of development position—writing code, architecting systems, tracking down bugs. So CV engineers also spend time consulting references, keeping up with developments in the field, and writing documentation.

Three Career Tracks in AI Are Available to Computer Vision Engineers

The field of artificial intelligence has dozens of sub-fields, like computer vision. But it’s also starting to diverge into three distinct types of career and educational tracks. Computer vision careers can follow any of those three tracks:

Pure research and development in new techniques and technologies for computer vision
General business and government uses of computer vision, turning theory into real-world applications
Specialized uses of computer vision in narrow areas of deep professional expertise

Most computer vision jobs will tend to fall into the second two categories, since that’s where most applied uses will be. But for the most qualified and best educated CV pros, there will be room at the cutting edge on the first track, exploring new methods and techniques that will feed the rest of the field.

Your Industry of Employment Will Channel Your Image Engineering Skills

Most companies hiring computer vision engineers today are vendors focused on creating solutions specific to various industries. Security systems to prevent retail theft, big tech companies like Apple developing new mobile camera filters and recognition systems, medical device makers building smart scanners, media companies developing special effects software… you name it, there are applications for computer vision all over.

Each of these industries have different demands on the computer vision engineering skillset. Custom clothing manufacturers need computer vision engineers with expertise in multi-camera 3D image generation to create production-ready patterns. Healthcare device manufacturers will want engineers who can work with images produced by x-rays and sonograms rather than light. Game companies will want professionals who can run the whole system in reverse, creating lifelike and believable imagery from bits and vectors.

This creates a series of unique ecosystems for computer vision engineer employment. Job titles are similarly diverse:

3D Computer Vision Developer
Data Scientist - Computer Vision Specialist in Detection Models
Research Engineer, Computer Vision
Senior Computer Vision Engineer
Machine Learning Engineer - Computer Vision
Computer Vision Algorithm Engineer

Naturally, your day-to-day experience, and the specific tasks in your role, will revolve around the needs of your industry.

When it Comes to Salary Potential, the Sky Is the Limit for Computer Vision Engineers

Of course, your paycheck will also reflect the standards of the industry you are working in. But if there’s one thing you can say about those rates, it’s that they will lean toward the top of whatever field you end up working in.

Like other kinds of jobs in the artificial intelligence world, it’s not so easy to pin down computer vision engineer salaries just yet. There’s no official job category in the list used by the Bureau of Labor Statistics to track American employment and salary data, for one thing.

Salary data for computer vision engineers are likely to fall into a couple of different categories within the existing fields listed for computer hardware engineers and software developers.

The average salaries for both of those roles are already lucrative:

But those are averages that include a lot of non-AI jobs. Anything in AI, including computer vision, is likely to see a hefty bump over the average.

For hardware engineers in the top ten percent of the profession, that is more than $208,200 per year. In the software developer group, it’s pretty close to that, at $198,100 annually.

You can find more details on our AI salary page.

Earning a Professional Certification in Computer Vision Engineering or Machine Learning Offers Career Boosts

One thing that is well-known to offer a boost to salaries in the technology community is professional certifications.

Different from an educational certificate, these are validations of your experience and expertise in a specific field or technical tool. Offered by vendors or industry associations rather than colleges, they signal employers that you have mastered a particular approach or toolset. That has real financial value, since they understand they are hiring someone who has proven they can get the job done.

In computer vision, there’s only one directly applicable certification offered today. The Certified Computer Vision Expert Certification from IABAC (International Association of Business Analytics Certification) goes into image filtering, object detection, transformation, segmentations, and other AI processes for image processing. The relevant training and tests are available from a variety of vendors.

You don’t necessarily need a specialized certification to boost your computer vision career, however. Many of the component skills and technologies in computer vision, from processing platforms to machine learning tools like TensorFlow, have their own associated certs. As building blocks toward professional competency, various machine learning certifications are well worth a look to get your career cooking.

What Are the Best Degrees to Earn for Computer Vision Engineers?

Computer vision is important enough that it exists as its own independent field of study in artificial intelligence. There is no shortage of degree programs that focus on tying together the skills and tools for developing visual systems.

Many Master of Science in Artificial Intelligence programs offer a concentration in Computer Vision. There are also dedicated Master of Science in Computer Vision degrees that go in-depth on computer vision, machine learning, and into even more exotic areas like multiple view geometry in computer vision.

Other specialized programs include Master of Engineering for Computer Vision and Control, which comes at the field more from the machine vision angle, and Master of Science in Engineering Robotics with Specialization in Computer Vision, which zeroes in on robotics uses and programming. In research, of course, a PhD in Computer Vision is unparalleled for building the most advanced qualifications.

While many employers prefer master’s graduates, a degree like a Bachelor of Science in Computer Vision together with a couple years of experience can still get you a job in the field. There’s also room for graduates with Bachelor of Science in Artificial Intelligence or Bachelor of Science in Machine Learning degrees to take on specific challenges in computer vision.

To pick up a specialization in computer vision after you’ve already earned a degree in a related field, you might turn to AI post-degree certificate programs. These are educational snapshots that consist of a few classes and usually less than a year of studying. They contain coursework to what you’d find in graduate degree programs, but don’t try to give you the full breadth. Instead, they build on your existing education and focus strictly on computer vision and pattern recognition knowledge.

A Visual Computing Graduate Certificate or a Signal and Image Processing Certificate can deliver exactly the coursework you need to turn a background in AI or computer science into qualifications for computer vision engineering jobs.

Unique Coursework and Research Come with Advanced Computer Vision Studies

Coursework in computer vision engineering tends to follow the same interdisciplinary path as the profession itself. Classes straddle the boundaries between the physics of vision and the computational mechanics used to interpret it.

A typical program will include classes like:

Computational Photography
Visual Learning and Object Recognition
Localization and Mapping
Visual Sensors
Geometric Methods in Computer Vision
Physics-based Rendering
3D Computer Vision

You’ll often have the opportunity to choose from among various elective options that will take you into specializations like:

And you’ll also have the core components of a general education in machine learning and artificial intelligence, including:

At the graduate level, you’ll also be engaging in research projects. A capstone or dissertation will allow you to structure your studies specifically around a project that lines up with your goals as a vision engineer. It also offers a chance to mingle and network at major industry events, like the IEEE Conference on Computer Vision and Pattern Recognition.

In a field that is so key to the future of artificial intelligence, you’ll have no shortage of opportunities as a computer vision engineer. But that doesn’t mean you won’t work hard. When you hit the wall on the toughest problems, you’ll be grateful for the education and preparation you put in along the way.

2022 US Bureau of Labor Statistics salary and employment figures for Computer Hardware Engineers and Software Developers, Quality Assurance Analysts, and Testers reflect national data, not school-specific information. Conditions in your area may vary. Data accessed December 2023.