How to Write a Computer Vision Resume (Step by Step With Examples)

Computer vision is a form of artificial intelligence (AI) that uses machine learning and neural networks to train computers how to handle visual images in areas such as facial recognition, spatial analysis, augmented reality, and more.

Computer vision is used in various industries, including manufacturing, health care, construction, sports, entertainment, and more. Its applications include detecting obstacles around autonomous vehicles, diagnosing illnesses, and checking the quality of products.

What do employers look for in a computer vision resume?

When hiring managers and potential employers review the experience and skills on your resume, they’re looking for a variety of technical and workplace skills.

Your technical skills and experience should include working with computer vision libraries like OpenCV and TensorFlow, programming languages like Java and C++, and machine learning models. You also need strong communication and problem-solving skills. Discover how to write a computer vision resume that showcases these skills and more.

Step 1: Create a computer vision resume template.

Decide on a resume template that showcases your information in a readable way while highlighting your experience and skills. You can choose from one of three main templates:

• Chronological resume: A chronological resume focuses on your experience, starting with your most recent experience chronologically and moving to older experience further down the page. This is a good option if you have previous work experience. Many employers prefer this template.

• Functional resume: A functional resume showcases your computer vision skills. It can be a preferable option if you’re a recent graduate or have little previous experience with the field of computer vision.

• Combination resume: A combination resume uses the best features of the chronological and functional formats to highlight your skills from your previous jobs. However, this template can create a lengthy resume. Most hiring managers want a resume that’s fewer than two pages.

Step 2: Fill in your computer vision resume essentials.

Make it easy for hiring managers and potential employers to reach you if they’re interested in your work and skills.

Include your name, email address, and phone number at the top of your resume to give them easy access to your contact information.

Depending on the job you’re applying for, it could be a good idea to include a website or online portfolio with examples of your work.

Step 3: Add your resume summary.

Your resume summary is a quick way to introduce yourself to your potential employer. It can include a summary of your previous professional experience and special skills.

Experienced professionals can use a resume summary to focus on previous jobs. If you're a recent graduate or new to the field, you can focus on your education and the skills you can bring to a professional setting.

Step 4: Showcase your computer vision skills.

Computer vision roles require you to have a variety of skills to be successful. Hiring managers may be looking for skills such as:

• Machine learning: Understanding of how machine learning can make decisions using visual data

• Programming languages: Experience with languages such as Python, Java, and C++.

• Technical skills: Familiarity with computer vision libraries such as OpenCV, TensorFlow, and PyTorch

It’s also important to have workplace skills such as communication to work with a team and problem-solving to find different ways to complete projects.

Step 5: Include your professional experience.

Your professional experience illustrates your previous work and the skills you used in each position. Start with your most recent experience and work down the page to your older experience. Include the company or organization you worked for, when you worked there, and your job title while there.

Follow each heading with a short description of your time at each position, including the skills you used in those positions, project goals you achieved, and other relevant information. These descriptions could also be a good place to include keywords that match words in the job description, including computer vision, which could make your resume stand out.

Step 6: Feature your certifications.

Certifications are a good option to add to your resume. They can show potential employers you’re proficient in the skills needed for computer vision jobs and that you’re proactively learning about the continuous changes in the field.

You can take courses that focus specifically on computer vision or other topics that relate to computer vision jobs, such as machine learning. You have options from colleges and universities and computer-related organizations such as OpenCV University. You can look for certificates in everything from basic courses covering computer vision to advanced options that cover different topics you might need to know about, such as TensorFlow.

Step 7: Include your education.

Include your education on your resume as well, especially if you are a recent graduate without much work experience. Include your educational institution, degree, and major. You can also include when you graduated.

To get a job as a computer vision engineer, you typically need at least a bachelor's degree. Popular majors include computer science, engineering, and mathematics [1].

Key takeaways

• Choose a resume template suited to your experience, skills, education, and other important information.

• Review your skills to make sure they match the job description and keywords.

• Make sure headings and descriptions are concise and clear.

• Limit your resume to two pages.

Jobs that use computer vision

You can use your computer vision skills in a variety of computer-related jobs, such as these roles (and average salaries) from ZipRecruiter:

• Computer vision engineer: $121,515 [2]

• Computer vision developer: $109,905 [3]

• Software engineer: $147,524 [4]

• Computer programmer: $64,974 [5]

Earn a certificate for your computer vision resume on Coursera

Boost your computer vision resume with additional education and certificates that can show potential employers you’re familiar with changes in the field of computer vision. 

Consider the Machine Learning Specialization with Stanford University and DeepLearning.AI on Coursera, where you’ll master fundamental AI concepts and develop practical machine learning skills.

Dive deeper and learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python with the IBM Machine Learning Professional Certificate on Coursera. This intermediate-level six-course series can help you master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles.