Computer Vision Engineers

Recruit Computer Vision Engineers

A computer vision engineer is a specialist in the merging of machine learning and human visual behaviour simulation. Their primary responsibility is to develop and automate computer vision models to improve our daily activities and lives. Hiring computer vision engineers is critical in the creation and testing of solutions for real-life applications. They work hand-in-hand with the technical team and clients to design and produce innovative products and services that consider real-world situations. Furthermore, they support prototype development and testing of cutting-edge technologies and ideas that could potentially become future products for the company.

Due to the increasing adoption by businesses for their operations and analytics, the difference between a computer vision scientist and a computer vision engineer is becoming harder to distinguish. With the technology’s advancement and the expanding range of computer vision applications, the roles of both scientists and engineers in this field are converging, making it challenging to tell the difference between them.

In certain instances, computer vision engineers in small businesses are responsible for researching new ideas as well as implementing them. To stay abreast and fully informed of the latest developments in this field, these engineers are required to systematically review and scrutinise research articles and emerging methodologies. It is vital that the job description of a computer vision engineer is carefully read and comprehended to ensure all necessary duties and responsibilities are met during the course of employment.

What does Computer Vision Engineering involve?

The area of computer vision is rapidly growing, with an unmet need for computer vision engineers. As per recent approximations, over 60,000 employment opportunities are accessible in the United States, with an expected increase in job openings over the coming years. Large corporations such as Amazon, Apple, Facebook, Google, and Rockstar Games are actively seeking computer vision experts, indicating the resilience and auspicious future of this profession in 2021 and beyond.

It is apparent that the evolution of computer vision is intricately correlated with the development of state-of-the-art technologies such as machine learning, deep learning, convolutional neural networks (CNN), TensorFlow, and PyTorch. As a result, the usage of computer vision tools is currently gaining momentum and is expected to be widely adopted in the upcoming years. This presents an excellent opportunity for professionals to refine their computer vision skills and keep up with the latest trends in this domain.

What are the duties and responsibilities of a Computer Vision Engineer?

As a Computer Vision Engineer, it is necessary to develop top-notch computer vision models, recalibrate them, design datasets and libraries of the utmost standards, and meticulously scrutinize research papers to discover novel solutions that align with the product’s objectives.

Startups and mid-sized businesses may occasionally list computer vision job postings and job descriptions as software engineering roles. It is crucial to thoroughly read the job posting as well as the employer’s requirements. It is conceivable that the need for an engineer to carry out software development and engineering duties along with computer vision duties may elucidate why companies employ broader job titles such as software engineer and software developer.

As a computer vision engineer, day-to-day obligations encompass a broad spectrum of tasks contingent on the organisation and the individual engineer’s expertise. These responsibilities usually consist of developing and implementing computer vision algorithms, scrutinising image data, devising imaging systems, and examining image processing outputs. Additionally, a computer vision engineer may be accountable for building and sustaining software, drafting technical reports, and providing technical guidance and support to other team members.

• Formulation of a computer vision model
• Automatable computer vision responsibilities comprise of collecting data for training purposes, detecting facial masks, tracking animals and calves in agricultural settings, and identifying unoccupied parking spaces.
• Inspect code and collaborate with specialists in data wrangling, machine learning, and data science.
• Developing and testing algorithms to articulate/quantify results through prototyping
• Revitalising or refurbishing images
• Establishing the foundation or groundwork
• Analysis of movement or motion
• Distinguished or recognised objects
• Scrutinising the most up-to-date journals and research papers.
  
  It is significant to bear in mind that most significant IT companies have a division of labour with role-specific activities. A computer vision engineer is liable for the overhaul and maintenance of the existing cutting-edge computer vision products established by leading tech companies. In smaller companies, a computer vision engineer is anticipated to handle multiple responsibilities, examining existing solutions and creating new ones. Startups similarly encourage comprehensive development, enabling engineers to broaden their expertise and competencies in all computer vision aspects. It is paramount to thoroughly examine the job description to grasp the job expectations.

What are the ways to obtain a Computer Vision Engineer position?

Individuals aspiring to be Computer Vision Engineers must possess a degree in computer science or engineering, which should preferably include a focus on computer vision and advanced machine learning techniques. This degree can be a Master’s, Bachelor’s, or Doctorate qualification. Along with essential technical skills, improving one’s curriculum vitae while applying for a computer vision engineer position can increase the likelihood of being hired. Additionally, the applicant should have the capability to write object-oriented code.

Masters, Bachelors, or Doctorate degrees in computer science or electrical engineering entail computer vision courses.

Accreditation and training in computer vision are helpful but not mandatory for a role as a computer vision engineer.

The above-mentioned educational requirements are not comprehensive and do not necessarily have to be met to enter the computer vision engineering profession. Numerous individuals have successfully transitioned into this field without having a STEM degree. The most crucial prerequisite is to possess eagerness to learn and make necessary efforts to achieve; any supplementary qualifications are just an add-on.

Requirements for a Computer Vision Engineer Profession

Numerous engineers might assume that transitioning to another profession is impossible, but this is not the case. It is never too late to learn new skills and acquire knowledge, regardless of an individual’s existing expertise level. Whether an individual is a student, novice recruit, or has worked in a particular profession for over a decade, they can benefit from upskilling. It is an ideal tactic to make a positive impact in their current job and progress in their profession. For those who want to pursue a profession in computer vision, below is a list of imperative skills required to succeed:

1. Capability to Build Machine Learning Models

   Computer vision involves an area of artificial intelligence and machine learning where deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and artificial neural networks (ANNs) are of utmost importance. An all-inclusive knowledge of these machine learning algorithms is indispensable for accurately identifying photographs and recognising objects.

2. Mastery in Programming Languages for Computer Vision

   Python, MATLAB, C++, and Cython are prevailing programming languages used for creating computer vision applications, each offering unique advantages and drawbacks. Python is well-known for its user-friendliness and abundant package library, while MATLAB is optimised for numerical computing and graphical visualisation. C++ is a potent general-purpose language extensively used in multiple industries, while Cython is a blend of Python and C++ that can provide better performance and improved integration with C/C++ libraries. Ultimately, the selection of the language is based on the specific requirements of the application.

3. Understanding of Image Processing Methods and Tools

   TensorFlow is an open-source machine learning framework created in 2015 by the Google Brain Team. It enables users to design and develop deep learning neural networks and other models that need substantial numerical computation. You Only Look Once (YOLO) is a technology used for real-time object detection, which uses Convolutional Neural Networks as its foundation. 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 employed for image manipulation and vast numerical evaluation and visualisation. It also offers Computer Vision ToolBox, a potent utility for creating and testing computer vision systems. Keras is an open-source deep learning framework coded in Python, which serves as a library wrapper for Theano and TensorFlow.

4. Mathematical Fundamentals

   Proficiency in linear algebra, three-dimensional geometry, and pattern recognition, fundamental convex optimisation, calculus of gradients, and Bayesian Probability are invaluable and much sought-after skills. Acquiring knowledge and expertise in these domains can be advantageous in solving intricate problems and making informed decisions.

5. Object Detection

   Object Detection is a computer vision method employed to identify objects in an image by utilising bounding boxes, which regulate the size and position of the objects in the picture. It varies from object localisation, as it can identify every instance of an object in the image.

6. Object Localization

   Object Localization is the technique of recognising the most prominent instance of an object in an image.

7. Object Recognition

   Tracking moving objects in a video or scene is useful in numerous scenarios, such as surveillance, special effects in movies, and autonomous vehicles. Two distinct approaches are involved in recognising and tracking relevant objects. The first approach is generative, which aims to identify areas in an image that closely resemble the target object while ignoring the background. On the other hand, the discriminative model focuses on distinguishing between the object and its surrounding environment.

How to Secure a Computer Vision Engineer Job?

Being a Computer Vision engineer, keeping abreast of current industry advancements and continuously developing one’s skills is crucial for achieving success. In order to achieve this, businesses must implement best practices efficiently and consistently. To ensure success, developers should consider two key factors. Firstly, it is beneficial to seek guidance from someone more experienced and knowledgeable when learning new skills and practising. Secondly, they must also focus on developing their analytical, programming, and soft skills. Therefore, having someone available for support if needed is essential.

Works offers opportunities for personal and career growth to skilled Computer Vision Engineers. Our remote Computer Vision positions provide a stimulating and rewarding environment, where you can take advantage of cutting-edge technology to solve complex commercial challenges. Joining our global developer community offers access to long-term and full-time remote Computer Vision engineer opportunities with highly competitive remuneration and clear paths for career advancement.

Description of the Job

Job Responsibilities

• Partner with data science engineers to oversee the handling and analysis of large volumes of data.
• Solve real-world problems utilising statistics, supervised learning, and computer vision libraries.
• Design, evaluate, and enhance deep learning and computer vision models to align with business requirements.
• Utilise image processing techniques to analyse unstructured data.
• Develop frameworks for image processing and visualisation, along with algorithms for image analysis.
• Automate predictive decision-making activities within the organisation.

Requirements

• Requirement for a Bachelor’s or Master’s degree in engineering, computer science, or information technology (or equivalent experience)
• Minimum of three years of experience required as a computer vision engineer (exceptions made for exceptionally skilled engineers)
• Expertise in deep learning algorithms and computer vision
• Required knowledge of object identification, tracking, as well as semantic or instance segmentation.
• Proficiency in machine learning frameworks, such as PyTorch, Keras, or Tensorflow
• Proficiency in training models using either NVIDIA or cloud technologies
• Essential knowledge of Python and related technologies, including PIL, NumPy, OpenCV, seaborn, and others.
• Comprehension of object-oriented programming languages and frameworks
• Fluent English required for collaborating with engineering management.
• Full-time work (40 hours per week) with a 4-hour time difference from US time zones.

Skills We Prefer

• Aptitude to transform data study conclusions into functional product models
• Expertise in GPU computing or cloud computing
• Command over data structures and algorithms in Python or C++ is essential.
• Comprehension of AWS, Azure cloud, and Internet of Things technologies