Computer Vision Engineers

Hire Computer Vision Engineers

A computer vision engineer is someone who specialises in the intersection between machine learning and the simulation of human visual behaviour. They are responsible for constructing and automating computer vision models that can assist us in our daily lives and activities. Computer vision engineers play a crucial role in developing and testing solutions for real-world problems and applications. They collaborate with the technical team and the client to design and create new products and services, taking into account actual input from the environment. Additionally, they support the formation and testing of prototypes for new technologies and ideas that may be transformed into actual products offered by the organisation in the future.

As the field of computer vision continues to grow and become more widely adopted by businesses for their analytics and operations, the distinction between a computer vision scientist and a computer vision engineer is becoming increasingly difficult to discern. As the technology develops and the scope of computer vision applications broadens, the roles of both scientists and engineers in the field are merging, making it difficult to differentiate between the two.

At times, computer vision engineers at small businesses are tasked with both researching new ideas and implementing them. To stay informed and up-to-date with current developments in the field, these engineers must regularly review and analyse research articles and new methodologies. It is essential that the job description of a computer vision engineer is carefully read and understood, in order to ensure that all necessary tasks are met during the duration of employment.

What does Computer Vision Engineering entail?

The field of computer vision is experiencing rapid expansion, with an unprecedented demand for computer vision engineers. According to current estimates, over 60,000 job opportunities are available in the United States, a number that is projected to continue its upward trend in the coming years. Major corporations such as Apple, Amazon, Facebook, Google, and Rockstar Games are actively seeking computer vision specialists, demonstrating the robustness of this career path and its bright outlook for 2021 and beyond.

It is evident that the advancement of computer vision is closely linked to the development of cutting-edge technologies such as machine learning, deep learning, convolutional neural networks (CNN), TensorFlow, and PyTorch. Consequently, the utilisation of computer vision tools is currently gaining traction and is expected to be widely adopted in the years to come. This presents a great opportunity for professionals to hone their computer vision abilities and stay abreast of the latest trends in the field.

What are the tasks and obligations of a Computer Vision Engineer?

As a Computer Vision Engineer, one must develop computer vision models, retrain them, create datasets and libraries of the highest quality, and review research papers to find innovative solutions that are pertinent to the product.

Startups and mid-sized companies may often categorise computer vision job postings and job descriptions as software engineering roles. It is highly recommended to read through the job posting as well as the employer’s expectations in detail. It is possible that the need for an engineer to execute software development and engineering tasks in addition to computer vision tasks may explain why organisations use broader job titles such as software engineer and software developer.

As a computer vision engineer, the typical day-to-day responsibilities involve a wide range of tasks that are determined by the particular organisation and the level of expertise of the individual engineer. These tasks typically include the development and implementation of computer vision algorithms, the analysis of image data, the design of imaging systems, and the evaluation of image processing results. Additionally, a computer vision engineer may also be responsible for creating and maintaining software, writing technical reports, and providing technical support and guidance to other team members.

  • Creation of a computer vision model
  • Computer vision tasks that can be automated include the collection of data for training purposes, the detection of facial masks, the tracking of animals and calves in agricultural settings, and the identification of vacant parking spaces.
  • Examine code and collaborate with machine learning and data science topic experts.
  • Prototyping and testing algorithms to articulate/quantify results
  • Image restoration
  • Setting the stage
  • Motion analysis
  • Identified objects
  • Examining the most current journals and research papers.

    It is worth noting that the majority of major IT companies have a division of labour and activities that are specific to certain roles. A computer vision engineer is responsible for the redesign and maintenance of existing computer vision products created by leading tech firms. In smaller businesses, a computer vision engineer is expected to wear multiple “hats,” evaluating existing solutions and designing new ones. Startups also tend to foster comprehensive development, allowing engineers to expand their skills and capabilities in all aspects of computer vision. It is essential to read the job description thoroughly to gain an understanding of the job requirements.

How can I get a job as a Computer Vision Engineer?

Computer vision engineers should possess a degree in computer science or engineering, preferably with a concentration in computer vision and advanced machine learning techniques. This degree can be either a Master’s, Bachelor’s or Doctorate qualification. In addition to the necessary technical skills, optimising one’s resume when applying for a role as a computer vision engineer can help to increase the chances of being hired. Furthermore, the successful candidate should have the ability to write object-oriented code.

Computer vision courses are included in a master’s, bachelor’s, or doctorate degree in computer science engineering or electrical engineering.

Accreditations and training in computer vision are advantageous, but not needed for work as a computer vision engineer.

The educational criteria outlined above are not an exhaustive list and do not need to be fulfilled in order to gain access to the profession of computer vision engineering. There are many individuals who have successfully transitioned into this field without having a degree in a STEM-related discipline. The most essential requirement is the eagerness to learn and put in the effort necessary to succeed; any additional qualifications are simply a bonus.

Qualifications for a Career as a Computer Vision Engineer

Many engineers may feel like they are unable to transition to a different career, but this does not have to be the case. It is never too late to acquire new skills and knowledge, regardless of your current level of expertise. Whether you are a student, a fresh recruit, or have been working in a particular profession for the last decade, you can always benefit from upskilling. It is a great way to make a positive difference in your job role and advance your career. For those who are interested in computer vision, below is a list of the essential skills needed for success in this field:

  1. Ability to Develop Machine Learning Models

    Computer vision is an area of artificial intelligence and machine learning that relies heavily on deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and artificial neural networks (ANNs). A comprehensive understanding of these machine learning algorithms is necessary for the successful identification of photographs and object recognition.
  2. Computer Vision Programming Language Expertise

    Python, MATLAB, C++, and Cython are widely used programming languages for the development of computer vision applications, each offering unique advantages and drawbacks. Python is renowned for its ease of use and its vast library of packages, while MATLAB is highly optimised for numerical computing and graphical visualisation. C++ is a powerful general-purpose language that is popular in many fields, while Cython is a hybrid of Python and C++ that can provide higher performance and improved integration with C/C++ libraries. Ultimately, the choice of language depends on the specific needs of the application.
  3. Knowledge of Image Processing Tools and Methods

    TensorFlow is an open-source machine learning framework developed by the Google Brain Team in 2015. It is designed to allow users to design and train deep learning neural networks and other models that require a large amount of numerical computation. YOLO (You Only Look Once) is a technology used for real-time object detection, which employs Convolutional Neural Networks as its base. OpenCV (Open Source Computer Vision) is a library for computer vision and image processing that is free and open source. MathWorks MATLAB is a programming language used for image manipulation and large-scale numerical analysis and visualisation. It also offers the Computer Vision ToolBox, a powerful tool for developing and testing computer vision systems. Keras is an open-source deep learning framework written in Python, which is a library wrapper for Theano and TensorFlow.
  4. Mathematical Roots

    Linear algebra, three-dimensional geometry and pattern recognition, basic convex optimisation, calculus of gradients, and Bayesian Probability are all highly advantageous and highly sought-after skills. Having knowledge and expertise in these areas can prove to be invaluable when it comes to solving complex problems and making decisions.
  5. Detection of Objects

    Object Detection is a computer vision technique used to recognise objects in an image by using bounding boxes, which control the size and positioning of the items in the picture. It is distinct from object localization, as it is capable of identifying all of the instances of the object present within the image.
  6. Localization of Objects

    Object localization is the process of identifying the single most visible instance of an item in a photograph.
  7. Object Recognition

    The tracking of moving objects in a scene or video has a multitude of applications, such as surveillance, special effects in movies, and autonomous vehicles. This process involves two distinct approaches for recognising and tracking the relevant objects. The first is a generative approach that attempts to identify areas in an image that most closely resemble the target object, while disregarding the background. Alternatively, the discriminative model searches for differences between the object and its immediate environment.

How can I get a job as a Computer Vision Engineer?

As a Computer Vision engineer, it is important to stay abreast of the current industry advancements and continually develop one’s abilities in order to be successful. To do this, businesses must ensure that they are applying best practices consistently and effectively. To ensure success, developers should bear in mind two things. Firstly, it is beneficial to seek guidance from someone more experienced and knowledgeable in the field when learning new skills and practicing. Secondly, they must also work on their analytical, programming, and soft skills. As such, it is important to have someone available to provide support if needed.

Working with Works provides experienced Computer Vision Engineers with an array of opportunities for personal and career growth. Our remote Computer Vision positions offer a stimulating and rewarding environment, where you can develop and utilise cutting-edge technology to solve complex commercial challenges. By joining our global developer community, you will have access to long-term and full-time remote Computer Vision engineer roles with highly competitive remuneration and clear paths for career advancement.

Job Description

Responsibilities at work

  • Collaborate with data science engineers to manage the processing and analysis of massive data volumes.
  • To tackle real-world issues, use statistics, supervised learning, and computer vision libraries.
  • Create, test, and optimise deep learning and computer vision models to suit business needs.
  • Analyse unstructured data using image processing methods.
  • Create image analysis algorithms and image processing and visualisation frameworks.
  • Automate the organisation’s predictive decision-making activities.


  • Bachelor’s/Master’s degree in engineering, computer science, or information technology (or equivalent experience)
  • At least three years of experience as a computer vision engineer is required (rare exceptions for highly skilled engineers)
  • Computer vision and deep learning algorithms expertise
  • Knowledge of object identification, tracking, semantic or instance segmentation is required.
  • Experience with machine learning frameworks such as PyTorch, Keras, or Tensorflow
  • Expertise in training models using NVIDIA or cloud technologies
  • Python and associated technologies such as PIL, NumPy, OpenCV, seaborn, and others are required.
  • Understanding of object-oriented programming languages and frameworks
  • English fluency is required for collaboration with engineering management.
  • Work full-time (40 hours a week) with a 4-hour time difference with US time zones.

Preferred skills

  • Capability to translate data study findings into operational product models
  • Knowledge of GPU computing or cloud computing
  • Data structures and algorithms in Python or C++ are required.
  • Understanding of AWS, Azure cloud, and Internet of Things technologies


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What makes Works Computer Vision Engineers different?
At Works, we maintain a high success rate of more than 98% by thoroughly vetting through the applicants who apply to be our Computer Vision Engineer. To ensure that we connect you with professional Computer Vision Engineers of the highest expertise, we only pick the top 1% of applicants to apply to be part of our talent pool. You'll get to work with top Computer Vision Engineers to understand your business goals, technical requirements and team dynamics.