Hire a Machine Vision Engineer

Modern businesses need to invest in quality control, identification and inspection processes to ensure they constantly produce the best products and services. For this, they require a robust and highly advanced system. Many businesses choose to invest in a machine vision system to cover all these processes.  

A simple way to understand the concept of machine vision is to think about it as the ‘eyes’ of a machine. Machine vision includes all applications in which a combination of hardware and software produces operational guidance to devices in executing their functions. This is usually based on the capture and processing of images. Elements that make up machine vision can include one or more video cameras, digital signal processing (DSP), and analog-to-digital conversion (ADC). 

Two important specifications for any machine vision product to work effectively are sensitivity and resolution. Resolution refers to the extent to which a machine can distinguish between objects. Usually, the better resolution, the more confined the field of vision will be. Sensitivity refers to the ability of a machine to see in low light conditions or to detect poor impulses at invisible wavelengths. 

Modern businesses are increasingly interested in quality inspection and automation in different verticals. This interest is likely to drive the market for machine vision. The international machine vision market size in 2020 was valued at 12.29 billion. In the period between 2021 and 2028, it is predicted to expand at a compound annual growth rate of around 6.9%. 

Commonly Used Tools and Frameworks in Machine Vision System

Because machine vision is a fast-growing field, many tools and resources are being developed to assist in engineering machine vision systems and their effective implementation. Some of these include: 

TensorFlow

TensorFlow is an open-source, end-to-end platform used for machine learning. It has a comprehensive and highly flexible ecosystem of tools and resources that allow businesses to push innovation in ML and enable ML developer teams to easily build and deploy ML-powered systems and applications.

PyTorch 

PyTorch is a machine learning framework that accelerates the journey from research to full production deployment. It is well supported on most major cloud platforms, offering frictionless development and effortless scaling.

FastAI 

FastAI is a popular open-source library mostly used for learning and practising deep learning and machine learning. It produces advanced results in standard deep learning domains and gives engineers low-level components that can be used to build brand-new approaches. 

Caffe2

This deep learning framework offers a straightforward way for engineers to experiment with deep learning and use community contributions of new models and algorithms.

Keras

Keras is an API that follows best practices for reducing cognitive load. It is designed to offer consistent and simple APIs, minimizing the number of user actions needed for common use cases. It also provides clear and actionable error messages.

Scikit-learn 

This tool is seen as the most robust library for machine learning in Python. It offers a large selection of effective tools for machine learning and statistical modeling via a consistent interface in Python.

MXnet 

MXNet is a tool used to define and deploy deep neural networks. It is flexible and super scalable, allowing for fast model training and supporting a flexible programming model and various languages.

Machine Vision Compared to Computer Vision and Machine Learning

When it comes to deep learning, cloud computing, and data integration systems, one can easily get confused between the functions of machine vision vs computer vision vs machine learning. However, it is important to understand the differences. 

Both machine vision and computer vision make use of image capture and analysis processes to perform tasks with speed and accuracy. However, there are important differences between computer vision vs machine vision. Machine vision is the methods and technology used to provide imaging-based automatic inspection and analysis for automatic inspection and process control. Machine vision actually makes use of computer vision in industrial environments. This ultimately makes it a subcategory of computer vision. So what is computer vision exactly?

Computer vision involves the automation of capturing and processing of images, with a focus on image analysis. In simple terms, computer vision’s goal is to see and present useful results based on observations. 

Machine learning (ML) is also closely related to machine vision and computer vision. ML is a branch of artificial intelligence and a method of data analysis used to automate analytical model building. ML is often used in the design and implementation of machine vision systems. 

The Roles and Responsibilities of MV Engineers for Businesses

Machine vision engineering is based on the intersection of artificial intelligence and machine learning. A machine vision engineer’s role is to help computers “see” through the use of machine learning, deep learning, and mathematical architectures in code. The role is highly technical and crucial in the success of producing high-quality automation equipment for businesses. 

Responsibilities of a machine vision engineer include being responsible for developing machine vision applications, liaising with design teams in specifying vision hardware requirements, performing all vision software programming activities, implementing new machine vision technologies, and managing the vision portions of projects to ensure timelines and targets are achieved.

The Benefits of Machine Vision Solutions and What ML Engineers Can Bring to Companies

Machine vision in AI and machine learning based on video annotations offer a quantitative measurement of a structured scene for businesses through speed, accuracy, and repeatability. Here are some of its most important benefits: 

• Improving product quality

Machine vision allows businesses to replace sample testing with top quality checks done through a camera system. This means that every batch produced can be reliably monitored for defects during the production process and without any interruption. The vision system offers a fast and thorough inspection that ensures a better product and reduced waste.

• Reduced cost

A machine vision system can enhance speeds and reduce scrap rates while scaling down the labor required to run the equipment. Overall, this leads to lower production costs.

• Enhanced safety 

Machine vision artificial intelligence systems bring additional safety to businesses by reducing human involvement in the manufacturing process. Furthermore, it limits human contamination of clean rooms and protects human workers from dangerous environments.  

For machine vision solutions to be successfully designed and implemented, businesses hire skilled and experienced machine learning or computer vision machine learning engineers. Some of the additional benefits businesses can take advantage of when having a knowledgeable ML engineer or team of engineers include: 

• Quality design 

ML engineers can help to design an effective machine vision system or computer vision applications according to a business’s specific needs and goals. Most engineers specializing in this field can assist throughout the system’s life-cycle, from initial planning to final testing and rollout. 

• Increased automation 

When machine vision software engineers combine ML with AI, they can create intelligent, consistently evolving systems. These machine vision technologies and automated systems can successfully increase efficiency and scalability.

• Effective data management 

An experienced ML engineer or computer vision AI developer can take good care of implementing proper and systematic management, as well as organization and storage of data on various platforms. 

• Rapid analysis prediction 

ML engineers specializing in computer vision engineer and deep learning fields can identify relevant data that makes it possible for businesses to take appropriate actions at the right time. 

The Skills and Responsibilities of Machine Vision Engineers 

Before businesses hire machine learning developers or machine vision engineers, it is important to understand exactly what type of skills and knowledge they require for their specific tasks. One way to research these skills is to look at example resumes of these engineers. 

Resume of a Mid-level MV Engineer 

Resume of a Senior MV Engineer

The Salaries of Machine Vision Developers

One of the most important considerations of hiring machine vision engineers and AI developers is the cost involved. In recent years, the demand for machine vision developer teams has increased significantly. The main drivers for this have been a growing need for quality inspection and automation. As a result, the machine vision engineer salary costs have also changed. Therefore, it is essential to do some research around the salaries of these professionals. 

During the research phase, it is crucial to study the salaries of different types of engineers. For example, those specializing in computer vision engineer vs machine learning engineer fields. Another important factor is keeping the salaries of engineers from different locations in mind, as these can vary considerably. Let’s explore the average annual machine vision and computer vision engineer salary from six different countries: 

• USA: $13,750 
• UK: $4,552
• Germany: $5,556
• Denmark: $6,249
• Netherlands: $4,018
• Ukraine: $3,100*

*Ukrainian salaries are provided based on Mobilunity’s Recruitment Team research on the local job markets. All salaries are net and do not include the service fee (in the case of hiring on a dedicated team model). The salaries are provided for comparison purposes and could not be entirely accurate. Contact us to know the exact cost of hiring a developer corresponding to the required parameters.

Looking at these figures, it can be concluded that hiring machine vision integrators and developers from Ukraine is the most affordable. In recent years, outsourcing to Eastern-European countries, including Ukraine, has increased dramatically. Businesses can now hire highly skilled and experienced software engineer machine vision developers at a third of the cost compared to other countries. This makes Ukraine one of the most sought-after countries to outsource from. 

Mobilunity as a Reliable Provider for Machine Vision Engineers in Ukraine

Mobilunity is a well-known and established outsourcing vendor in Ukraine. We have more than 10 years of experience providing machine vision and computer vision engineer professionals for businesses around the world. When you hire a machine vision engineer from our team, you gain access to their expertise in automotive software development services, ensuring robust and innovative solutions tailored to your specific needs.

Our expert team hires skilled and knowledgeable candidates to work on machine vision consulting services and development projects of all sizes. As one of the most experienced computer vision companies in outsourcing, we also use time-tested methods to recruit and vet the most experienced workers for clients. 

Through the years, Mobilunity has worked with many reputable clients around the world, including CamptoCamp (Switzerland), Paidy (Japan), DNest (Spain), and ZenChef (France).

Our goal is to successfully boost the operations of our clients. To do this, we follow the highly efficient process below: 

• We go out of our way to choose only the top candidates suited to our clients’ ideal candidate profiles.
• Our team ensures all our workers are set up with the best tools for maximum efficiency and productivity. 
• We ensure all contractual obligations are met throughout the process and that clients’ requirements are satisfied.

Looking for software engineer machine vision specialists? Talk to the expert team at Mobilunity and find the right engineers effortlessly!

Written by:

Anastasia Stefanuk

Market Researcher

Disclaimer: All salaries and prices mentioned within the article are approximate NET numbers based on the research done by our in-house Recruitment Team. Please use these numbers as a guide for comparison purposes only and feel free to use the contact form to inquire on the specific cost of the talent according to your vacancy requirements and chosen model of engagement.