How to write a computer vision engineer job description

Computer vision engineers enable machines to extract information from images and videos. Their work is transforming industries. It’s enhancing security systems, healthcare diagnoses, interactive entertainment, and augmented reality.

You must craft a great job description to find the best candidates for this highly specialized role. But this involves accurately conveying the technical skills, soft skills, and industry-specific knowledge required for the role – a difficult task.

We can help simplify this process. In this article, we explain the main skills to focus on and mistakes to avoid while writing a computer vision engineer job description.

What is a computer vision engineer?

A computer vision engineer develops algorithms and systems that enable computers to understand visual data, including images and videos.

Using machine learning and artificial intelligence, they craft software that can recognize patterns, objects, and scenes. This technology powers various applications, including facial recognition systems, augmented reality, and autonomous vehicles.

By extracting useful information from visuals, computer vision engineers bridge the gap between human vision and machine perception.

Key skills to look for in computer vision engineers

Here are some must-have skills for a computer vision engineer.

Hard skills

• Proficiency in programming languages including Python, C++, or Java, focusing on algorithms and data structures applied explicitly in computer vision projects

• Knowledge of machine learning and deep learning frameworks, including TensorFlow or PyTorch

• Knowledge of neural networks – especially convolutional neural networks (CNNs)

• Understanding of image processing techniques – including filtering, edge detection, and image segmentation– for manipulating images

• Familiarity with computer vision libraries, like OpenCV or Dlib, for developing and implementing vision-based models

• Mathematical skills in linear algebra, calculus, and statistics for algorithm development and data analysis 

• Knowledge of sensor fusion for integrating data from multiple sensors (cameras, LiDAR, and so on) to create comprehensive visual understanding systems

Soft skills

• Problem-solving abilities for determining innovative solutions to the complex problems common to computer vision engineering – such as dealing with low-light conditions or addressing issues with object detection

• Attention to detail for ensuring precision in coding and model training – and enhancing the accuracy and reliability of computer vision applications

• Collaborative skills for working effectively with data scientists, software developers, and project managers

• Adaptability, including a willingness to stay updated with the rapidly evolving field of computer vision and learn new techniques and technologies as they emerge

• Communication skills for explaining technical project requirements, progress, and outcomes to non-technical stakeholders

How to write an effective computer vision engineer job description

Follow these four best practices to create a detailed computer vision engineer job description. This will help you attract candidates with the right mix of skills and knowledge. Plus, it will give them a realistic understanding of the role.

1. Define core computer vision challenges

Detail the specific computer vision challenges your company aims to solve, such as enhancing object detection algorithms for crowded scenes or improving the accuracy of real-time motion tracking systems. 

This helps candidates understand the scope of tasks they’ll be working on.

Here’s what this could look like: “Candidates will tackle advanced object detection challenges in dynamic environments, aiming to double our system’s accuracy in identifying and tracking moving objects.”

2. List specialized knowledge

Clearly mention the specialized knowledge that’s crucial for the candidate’s success. This could include expertise in 3D computer vision techniques, familiarity with deep learning approaches for image classification, or knowledge of specific datasets relevant to your projects – for example, satellite data.

For example, you might say something like this: “Applicants must possess deep knowledge of 3D reconstruction techniques and know how to work with simultaneous localization and mapping (SLAM) algorithms.”

3. Highlight the integration of computer vision with other technologies

Indicate how the role integrates computer vision technology with other key technologies, such as Internet of Things (IoT) devices, robotics, or augmented reality systems. This shows candidates the interdisciplinary nature of the work and the breadth of skills they can develop.

For instance, a job description might say, “The selected candidate will design computer vision systems that integrate seamlessly with our IoT ecosystem, enabling smarter home security solutions.”

4. Emphasize collaboration with other teams

Outline how the computer vision engineer will collaborate with various teams and people – including AI researchers, software developers, and product managers. This underscores the importance of communication skills and teamwork. 

For example, a clear job description could say, “The computer vision engineer will work closely with the AI team to integrate vision-based models into broader machine learning systems, enhancing the intelligence of autonomous drones.”

Computer vision engineer job description template

Here’s a customizable computer vision engineer job description template you can use to write your own.

Company introduction

Introduce your company by describing your industry, core products and services, work culture, and goals. Also, note any recent awards or publications showcasing your authority in the industry.

Briefly describe the role, explaining how the computer vision engineer’s work will help your company reach its goals. 

Benefits of working with [your company]

Mention benefits that will appeal to computer vision engineers. After the basics – like health insurance or bonuses – list any perks specific to the role. For instance, you might include opportunities to work on cutting-edge projects that can revolutionize your industry or participate in expert-led professional development programs.

Computer vision engineer job brief

[Company name]

Job title: Computer vision engineer

Reports to: [For example, senior engineer or program manager]

Position type: [Full-time, part-time, or contract]

Location: [Remote, hybrid, or on-site (specify address if applicable)]

Salary and benefits: [Competitive salary package, including benefits like health insurance and retirement plans]

Responsibilities and duties

• Design and develop advanced computer vision algorithms for object detection, recognition, and tracking.

• Implement and optimize algorithms for real-time processing and performance efficiency.

• Collaborate with cross-functional teams to integrate computer vision systems into broader product architectures.

• Conduct ongoing research to stay ahead of the curve in computer vision tools and methodologies.

• Test and refine models and systems based on real-world data and feedback.

Skills and qualifications

Required skills and experience

• Bachelor’s or master’s degree in computer science, electrical engineering, or a related field – or equivalent experience training and/or experience 

• Proven ability to develop computer vision applications using Python, C++, and relevant frameworks such as OpenCV, TensorFlow, or PyTorch

• Solid understanding of machine learning, neural networks, and image processing techniques

• Knowledge of real-time computer vision and video analytics solutions

• Strong critical thinking, analytical, and problem-solving skills

Preferred skills and experience

• PhD in a relevant field

• Experience with 3D computer vision and techniques, such as SLAM

• Publications in major journals or conferences in computer vision or related fields

• Knowledge of additional programming languages or tools

3 things to avoid when writing a job description for computer vision engineers

Here are three common mistakes you must avoid in your computer vision engineer job description.

1. Ignoring the balance between research and application

Not clarifying how much time the engineer will spend researching versus applying their knowledge is a mistake. 

In computer vision roles, the balance between theoretical research and practical application can vary significantly. Some positions may be heavily research-oriented, focusing on developing new algorithms and techniques. Others might be more application-focused, involving the implementation of existing solutions to solve real-world problems. 

Failing to specify this balance can attract candidates with mismatched expectations, leading to dissatisfaction or underperformance.

2. Not specifying the complexity of data and models

Computer vision data can range from simple 2D images to complex 3D structures. Also, the models can vary from traditional machine learning to advanced deep learning architectures.

Specifying the complexity of the data types and models the engineers will work with is crucial for attracting candidates with the knowledge you need – or a willingness to learn. For instance, engineers who know how to handle simple 2D image data might not be proficient in dealing with 3D data complexities, which often require understanding of spatial dimensions and advanced processing techniques. 

By clearly stating the expected data types and model complexities, you can attract candidates who are better prepared for the position’s specific challenges.

3. Overlooking the importance of industry-specific knowledge

Assuming all computer vision expertise is interchangeable without recognizing the importance of industry-specific knowledge can lead to mis-hires. 

Computer vision applications in different industries – like healthcare, automotive, retail, or security – vary significantly and must abide by unique regulatory standards. Because of this, you must try to attract candidates with industry-specific expertise – or a strong desire to learn. 

Next steps: Attract and assess computer vision engineer candidates

Once you’ve created a detailed job description and posted it on all relevant avenues, including social media and job boards, you must focus on assessing applicants. Talent assessments can streamline this process.

TestGorilla is a talent assessment platform that offers role-specific tests for evaluating candidates for the computer vision engineer role. Here are six tests you might use to assess candidates for this position:

• Our Computer Vision test uses real-world problems to assess how candidates conceptualize and implement computer vision solutions.

• The C++ Language-Specific Concepts test evaluates candidates’ ability to use C++ to solve software development problems.

• Our Python (Working with Arrays) test measures candidates’ ability to create short algorithms that involve arrays.

• The PyTorch test sees how candidates use this framework to implement deep learning models.

• TestGorilla’s TensorFlow test assesses candidates’ proficiency in TensorFlow fundamentals, functionalities, and more.

• Finally, our Artificial Intelligence test measures candidates’ understanding of AI concepts, plus their ability to tackle business challenges using AI.

The computer vision engineer position varies depending on the company and its needs. Luckily, TestGorilla enables you to create custom talent assessments for your company’s role. You can combine up to five tests and even include personalized questions.

Apart from hard skills, you can also examine candidates’ personality traits and soft skills using personality and culture tests, cognitive ability tests, situational judgment tests, and more. Combining multiple test types gives you a holistic view of each candidate’s skills and how they’ll fit in at your company.

FAQs

Seeking more information? Check out these commonly asked questions.

What is the difference between a computer vision engineer and a software engineer?

A computer vision engineer specializes in building and refining systems that enable computers to interpret visual data. They focus on object detection, image recognition, and real-time video analysis. 

In contrast, a software engineer designs, develops, and maintains software applications using programming languages and software development methodologies.

How much does a computer vision engineer make?

According to Indeed, the average salary for a computer vision engineer in the US is $128,436/year as of February 2024. However, this can vary by company and also depends on location, seniority level, and other factors.

Hire the best computer vision engineers with TestGorilla

Crafting a clear computer vision engineer job description is the first step to hiring top engineers who can leverage AI and machine learning to capitalize on visual data and find innovative solutions.

The next step is assessing your applicants. TestGorilla has hundreds of tests that can help you accurately rank your applicants’ skills without bias. Using our tests, you can find the best computer vision engineers.

Learn more about TestGorilla by booking a live demo or signing up for a free account today.