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Hiring Machine Vision Engineers - a comprehensive guide

Anudeep Rastogi

Machine vision has emerged as a leading technology for automated visual inspection in the global manufacturing industry. Markets and Markets reported that the market size for machine vision is expected to grow from $10.7bn in 2020 to $14.7bn by 2025 at a compounded annual growth rate of 6.5%!

This blog serves as a guide for understanding the process involved in automation using Machine Vision and how to hire a Machine Vision Engineer.

What is Machine Vision? What are its applications?

Machine vision refers to a computer's ability to perceive and interpret its environment through the use of one or more video cameras, which are equipped with analog-to-digital conversion and digital signal processing.

The image data captured by the cameras is transmitted to a computer or robot controller for further analysis.

Unlike the human eye, which can only detect electromagnetic wavelengths from 390 to 770 nanometers, machine vision systems can sense a much wider range of wavelengths, including infrared, ultraviolet, and X-ray.

Machine vision is often associated with a computer's ability to "see" and is referred to as computer vision technology. This technology involves digitizing an image, processing the data, and taking some form of action based on the analysis.

Machine vision systems are commonly used in various industrial processes such as material inspection, object recognition, pattern recognition, electronic component analysis, signature recognition, optical character recognition, and currency recognition.

Apart from these, machine vision systems have several other applications like:

  • Object detection: On the machine side, component developments are giving much improved raw materials, such as a more extensive range of cameras used to create particular picture capturing solutions, new lenses, complicated robotics, and more.
  • Measurement: As the name suggests, Measurement apps are used to determine the exact dimensions of items and are done by locating specific points on a photograph and obtaining geometrical measures from it.
  • Flaw Detection: Flaw detection software detects surface flaws, dents, and scratches on a product's surface. Flaw detection apps must be rigorously objectified to separate "acceptable" problems from intolerable faults. Artificial intelligence-based machine vision is excellent for these applications since instances train the system rather than "rules."
  • Print defect identification: The purpose of print defect identification is to locate printing anomalies such as incorrect color shades or missing or defective sections of the print.
  • Identification: It entails identifying a part or product to trace it throughout the manufacturing or logistics process to ensure that the correct item is produced. Reading characters (OCR) or barcodes can be used to identify objects.
  • Locating: It is routinely utilized to find things in applications like robotic guidance. Its purpose is to determine the coordinates and location of a target object. Its data can pick up the object or do any other task requiring this position. The machine vision application needs its system to be taught the child component of interest to recognize the part during manufacture.
  • Counting: Counting is the use of it to count things of interest, as the name indicates.

In summary, machine vision may be beneficial to any industrial facility with a repeated procedure. It is widely used in various sectors, including automotive, plastics, food and packaging, medical devices, and electronics.

What are the differences between Machine Vision and Computer Vision?

Machine vision and computer vision are often used interchangeably, but there are some differences between the two:

  • Computer vision is a broader field that encompasses a range of techniques for enabling computers to interpret and understand visual data from various sources such as images, videos, and 3D models. Computer vision techniques involve advanced algorithms and mathematical models to extract features, identify patterns, and classify objects. Applications of computer vision include facial recognition, augmented reality, autonomous vehicles, and medical imaging.
  • Machine vision is a subset of computer vision that specifically refers to the use of cameras and image sensors to capture visual data from the physical environment. The data is then processed and analyzed by specialized software to perform automated tasks such as object detection, recognition, measurement, and inspection. Machine vision is commonly used in industrial applications such as quality control, robotics, and automated manufacturing.
  • Computer vision systems are commonly used to extract and use as much data as possible about an object. In contrast, machine vision systems typically focus on specific parts or critical parts of an object and then process that data from its image capture. Since it is used more to find specific qualities, machine vision is normally used for fast decisions in a controlled environment
In summary, machine vision is a specialized application of computer vision that focuses on automated visual inspection and analysis using cameras and sensors.

Computer vision, on the other hand, is a broader field that includes machine vision and other advanced techniques for visual perception and understanding. Regardless of the differences between the two, the applications of both computer vision and machine vision technologies are immensely diverse.

What are the components of a Machine Vision System? How does a Machine Vision System work?

A machine vision system, also known as an automated vision or inspection system, comprises several common components. Although each component has its specific function, they work in unison to achieve the machine vision system's objectives. The five fundamental components of a machine vision system are:

  1. The lighting system
  2. The lens
  3. The sensor
  4. The camera
  5. The communications system

A machine vision system functions by using a sensor to detect the presence of an object, such as in the case of product inspection. Once the sensor detects the object, a camera is triggered to capture an image and a light source illuminates the key features of the object.

The camera's image is then converted into a digital output by a frame-grabber, which stores it in the computer's memory for processing by software. The image is first converted to a binary format with black and white gradations.

The system's software then analyzes the image to identify defects and proper components according to predetermined criteria. Based on the findings of the machine vision system, the product will either pass or fail inspection.

To summarize, Machine vision systems can either consist of discrete elements or be integrated into a single unit, such as a smart camera that combines the functionalities of individual components into a cohesive package. The effectiveness of a machine vision system depends on the quality of the components being evaluated. A more consistent component placement and orientation can lead to better system performance.

What does a Machine Vision Engineer do?

A machine vision engineer is a professional who specializes in designing and developing machine vision systems, which are used to analyze and interpret visual data from images or video. These engineers use a combination of hardware and software technologies to create systems that can perform tasks such as object recognition, tracking, and measurement.

Machine vision engineers may work in a variety of industries, including manufacturing, healthcare, automotive, robotics, and security. They may also work on projects involving artificial intelligence, machine learning, and data analysis.

One of the primary tasks of a machine vision engineer is to develop and optimize image processing algorithms to extract relevant information from images or videos. They also need to design and integrate hardware components, such as cameras and sensors, to acquire the necessary data. In addition, they work on developing and training machine learning models to enable the systems to identify and classify objects.

Machine vision engineers typically work in interdisciplinary teams that include mechanical, electrical, and software engineers, as well as data scientists. They also need to keep up-to-date with the latest developments in computer vision technologies and participate in continuous learning and professional development activities.

In summary, a machine vision engineer is a skilled professional who plays a critical role in designing, developing, and maintaining machine vision systems that are used in a wide range of industries.

What are the roles and responsibilities of a  Machine Vision Engineer?

The specific roles and responsibilities of a machine vision engineer can vary depending on the industry they work in and the specific project they are working on. However, some common responsibilities of a machine vision engineer include:

  1. Designing and developing machine vision systems: Machine vision engineers are responsible for designing and developing computer vision systems that can analyze and interpret visual data from images or video.
  2. Selecting hardware and software components: Machine vision engineers must select the appropriate hardware and software components needed to create a system that meets the project requirements.
  3. Developing algorithms and software: Machine vision engineers must develop algorithms and software that can analyze and interpret visual data from images or video.
  4. Testing and debugging: Machine vision engineers must test and debug the system to ensure that it is functioning properly and meeting the project requirements.
  5. Collaborating with other engineers: Machine vision engineers may collaborate with other engineers, such as mechanical engineers, electrical engineers, and software engineers, to ensure that the system is integrated properly with other systems.
  6. Troubleshooting: Machine vision engineers must troubleshoot problems that may arise with the system and find solutions to fix them.
  7. Staying up-to-date with industry developments: Machine vision engineers must stay up-to-date with the latest developments in machine vision technology and incorporate new technologies into their work when appropriate.
  8. Documenting design and development processes: Machine vision engineers must document the design and development processes used in the creation of the system, including any challenges encountered and solutions implemented.

What skills do Machine Vision Engineers have?

Machine vision engineers require a diverse range of skills in order to be successful in their role. Some of the essential skills that a machine vision engineer should possess include:

  • Strong knowledge of computer programming languages: Machine vision engineers should be proficient in programming languages such as C++, Python, and MATLAB, as they will be working with software that requires coding.
  • Understanding of image processing techniques: Machine vision engineers must have a good understanding of image processing techniques, such as filtering, segmentation, and feature extraction.
  • Familiarity with hardware components: Machine vision engineers should be familiar with hardware components such as cameras, sensors, and lighting systems, as they will be working with these components to create the machine vision system.
  • Knowledge of statistical analysis: Machine vision engineers should have a good understanding of statistical analysis and how it applies to machine learning and computer vision.
  • Experience with machine learning algorithms: Machine vision engineers should have experience with machine learning algorithms, such as neural networks, support vector machines, and decision trees.
  • Attention to detail: Machine vision engineers must have excellent attention to detail, as the smallest error can cause the system to fail.
  • Problem-solving skills: Machine vision engineers must be able to troubleshoot problems that may arise with the system and find solutions to fix them.
  • Strong communication skills: Machine vision engineers must have excellent communication skills, as they will be collaborating with other engineers and stakeholders to ensure the system is meeting the project requirements.
  • Ability to work in a team: Machine vision engineers must be able to work effectively as part of a team, as they will be working with other engineers and stakeholders to develop and implement the machine vision system.
  • Continuous learning: Machine vision engineers must have a strong desire to continuously learn and stay up-to-date with the latest advancements in machine vision technology.

What tools and technologies do  Machine Vision Engineers know and use?

Apart from having aforementioned skills Machine vision engineers use a wide range of tools and technologies to develop and implement computer vision systems. Some of the common tools and technologies used by machine vision engineers include:

  • Programming languages: Machine vision engineers use programming languages such as C++, Python, and MATLAB to develop software that processes visual data.
  • Integrated Development Environments (IDEs): IDEs such as Visual Studio, Eclipse, and PyCharm provide a development environment for writing and debugging code.
  • Image processing libraries: Image processing libraries such as OpenCV and MATLAB's Image Processing Toolbox provide a wide range of tools for analyzing and processing visual data.
  • Machine learning frameworks: Machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn provide a platform for developing machine learning algorithms that can be used for object detection, recognition, and classification.
  • Hardware components: Machine vision engineers work with a range of hardware components, including cameras, sensors, and lighting systems, to capture and process visual data.
  • 3D modeling and simulation software: 3D modeling and simulation software such as Blender and Unity can be used to create virtual environments for testing and validating machine vision systems.
  • Robotics software: Robotics software such as Robot Operating System (ROS) provides a platform for developing and controlling robotic systems that incorporate machine vision technology.
  • Cloud computing platforms: Cloud computing platforms such as Amazon Web Services (AWS) and Microsoft Azure provide scalable computing resources for training machine learning models and processing large amounts of visual data.
  • Augmented reality and virtual reality software: Augmented reality and virtual reality software such as Vuforia and Unity can be used to develop immersive experiences that incorporate machine vision technology.
  • Data visualization tools: Data visualization tools such as Tableau and Power BI can be used to create visual representations of the data generated by machine vision systems

Which companies are really known for innovations in Machine Vision?

Perhaps the most well-recognized application of machine vision is in the first robot of its kind, the Spot autonomous four-legged robot from Boston Dynamics (Waltham, MA, USA) which now offers on-board artificial intelligence software to process data and draw insights out of the environment while keeping human operators out of hazardous environments.

In September 2019, Boston Dynamics released Spot to the world as its first commercial product to enable non-academic and non-military users to explore what this type of nimble, four-legged robot can do as a commercial application. While early adopters successfully deployed Spot robots for data collection, knowing how to understand that information and turn it into actionable insights quickly became a challenge.

Apart from Boston Dynamics here are some of the great companies which work with and develop machine vision systems:

  • Pleora Technologies, Inc. is a manufacturer of quality inspection, machine vision, rugged networking systems, and software for industrial applications. Products include turnkey visual inspection systems, connectivity devices, gateways, frame grabbers, network interfaces, and video switchers.
  • ISRA VISION is a custom manufacturer of machine vision systems including surface vision systems, quality inspection systems, inline gauging systems, robot vision sensors, in-process analysis software, and gauging database management software. 
  • Dartronics, Inc. is a distributor of end-of-line packaging, marking & coding equipment & supplies. Types of equipment & supplies include conveyors, carton erectors, case erectors, case packers, palletizers, horizontal flow wrappers, stretch wrappers, and much more.
  • VISIONx Inc. is a manufacturer of machine vision, image analysis, visual inspection, general defect detection, and metrology software and hardware including digital comparators, 3D inspection and measurement systems, visual/video inspection systems, and much more.
  • Predator Software Inc. is a manufacturer of industrial software and hardware. The software includes applications for manufacturing systems management, distributed numerical control (DNC), data management, CNC coding and simulation, tool tracking, data collection and visualization, and utility software.
  • Omron Automation Americas is a manufacturer of industrial automation systems, equipment, and components. Products include controllers and human-machine interfaces, control and switching components, rotary, photoelectric and ultrasonic sensors, switches, motors, robots, servo, and programmable safety systems. The company's annual revenue shown in table 1 is taken from Omron Automation's global HQ in Kyoto, Japan.
  • QxSoft, LLC is a manufacturer of metrology software for coordinate measuring machines (CMM), vision systems, portable arms, measuring microscopes, automatic cells, and gear inspection.
  • Keyence Corp. of America is a manufacturer of machine vision systems. The company's products include stand-alone systems that can process up to 4 cameras at a time. The company's annual revenue shown in table 1 is taken from Keyence Corp.'s global HQ in Osaka, Japan.
  • Eigen Innovations is a design, fabrication, and installation company of AI-enabled automated vision inspection systems and software for the automotive, manufacturing, and packaging industries.

Targeting these companies for Machine Vision Engineers is a good place to start :)

What is the compensation range for Machine Vision Engineers?

According to Glassdoor, the salaries of Machine Vision Engineers in the US range from $67,200 to $100,800 , with a median salary of $84,000 .

Boolean search for finding  Machine Vision Engineers

A generic boolean search string for finding Machine Vision Engineers around terms looks like:

  • -job -jobs -sample -examples, to exclude irrelevant results
  • (intitle:resume OR intitle:cv) to discover candidates’ online resumes or CVs
  • (“machine vision engineer” OR “automation architect”) to cover variations of the same job title

Here’s an example of a simple string to find resumes:

(intitle:resume OR intitle:cv) (“data administrator” OR “database analyst”) -job -jobs -sample -templates

With this search string, the words “resume” or “CV” have to appear in the page title. Adding variations of machine vision job roles provides a larger number of relevant results. And, excluding more terms will reduce false positives.

Let’s look at what a final Boolean search looks like using the following fields:

  • Job title: (“Machine Vision Engineer” OR “Machine Vision” OR “OpenCV Engineer”) AND (“Senior” OR “Lead” OR “Team Lead”)
  • Sector: (“Healthcare” OR “Automotive”)
  • Image Processing: (“2D” OR “3D” OR “Video Processing”)
  • Tech Stack: Python, C++

The Boolean search string that can be created using the aforementioned fields, applicable to any job board, would resemble the following:

(“Machine Vision Engineer” OR “Machine Vision” OR “OpenCV Engineer”) AND (“Senior” OR “Lead” OR “Team Lead”) AND (“Healthcare” OR “Automotive”) AND (“2D” OR “3D” OR “Video Processing”) AND Python AND C++

Similarly, some of the complete boolean strings to find Machine Vision Engineers in a particular location, with specific skills etc. are:

  • Location - ("Machine Engineer" OR "Machine Vision Developer" OR "Automation Lead") AND ("Machine Learning" OR "AI" OR "Artificial Intelligence") AND ( Bentonville OR Dallas OR Sunnyvale) NOT (.NET)
  • Tech Stack - ("Machine Engineer" OR "Machine Vision Developer" OR "Automation Lead") AND (storm OR elasticsearch OR OpenJDK OR OpenCV)

By using Boolean search as shown above in combination with other research methods, you can greatly increase your chances of finding the right machine vision engineer for your project. Good luck with your sourcing!

What are some sample Interview Questions for Machine Vision Engineers?

Here are some examples of interview questions to ask:

Hard Skills

Logic & Algorithms
  1. How can you evaluate the predictions in an Object Detection model?
  2. What are the main steps in a typical Machine Vision pipeline?
  3. Apply deep learning for quality inspection on a conveyor belt with a machine vision system?
  4. What is a  Kalman filter? Explain its application in the Machine Vision system?
  5. How do Neural Networks distinguish useful features from non-useful features in Machine Vision?
  6. How does Image Registration work?
  7. How to detect Edges in an image?
  8. How would you decide when to grayscale the input images for a Machine Vision problem?
  9. How well do you understand the differences between image processing and pattern recognition?
  10. Write an algorithm of an Image Noise Filter?
  1. What are the fundamental components of any machine vision system
  2. How will you create a Machine Vision inspection system for bad chocolate in a chocolate factory?
  3. Why does one use MSE as a measure of quality? What is the scientific/mathematical reason for the same?
  4. What are convolutional neural networks (CNNs)?
  5. How would you go about training a neural network to recognize objects from an image?
  6. Describe how you use mathematics for machine vision tasks.
  7. Provide an intuitive explanation of how The Sliding Window approach works in Object Detection
Programming Languages & Tools
  1. What is a Robot Operating System? Explain its features and components?
  2. What is difference between shallow copying and deep copying
  3.  Describe your experience with C++, Python, Java or other programming languages.
  4. What are some of the OpenCV Library modules?
  5. What are the latest trends in Machine Vision programming languages?
  6. What languages and frameworks are used to build projects involving 3D imaging or augmented reality?
  7. Write a program in a language of your choice to detect grayscale in an image using OpenCV

Behavioral / Soft Skills

  1. What are some of the most important skills for a machine vision engineer to have?
  2. How would you go about designing an autonomous machine in the print industry?
  3. What is your experience with machine vision related algorithms?
  4. Provide an example of a time when you had to troubleshoot an issue with a machine vision system.
  5. Do you have any experience with machine learning? If so, what applications have you used it for?
  6. When working on a new project, what is your process for determining requirements?

Hope you found this guide useful and good luck on your recruiting!

About Rocket

Rocket pairs talented recruiters with advanced AI to help companies hit their hiring goals and knows technology recruiting inside out. Rocket is headquartered in the heart of Silicon Valley but has recruiters all over the US & Canada serving the needs of our growing client base across engineering, product management, data science and more through a variety of offerings and solutions.

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