We, at Turing, are hiring remote Computer Vision engineers who can research, analyze and process large volumes of data using computer vision and segmentation techniques and automate predictive decision-making efforts. Accelerate your career by working with elite U.S companies from the comfort of your home.
Apply to Turing today.
Fill in your basic details - Name, location, skills, salary, & experience.
Solve questions and appear for technical interview.
Get matched with the best US and Silicon Valley companies.
Once you join Turing, you’ll never have to apply for another job.
A computer vision engineer works at the intersection of machine learning and human-like visual simulation. He's in charge of creating and automating computer vision models that help us do our jobs and live more comfortably. Computer Vision engineers are in charge of creating and testing solutions for real-world challenges and applications. They also collaborate with the technical team and the customer to develop new products and services while taking real-time feedback into account. In addition, they assist in the development and testing of prototypes for new technologies and concepts that may one day become full-fledged goods that the firm may provide.
The boundary between a computer vision scientist and a computer vision engineer is becoming increasingly blurry as the computer vision domain expands and more firms embrace computer vision business and analytics.
At times, computer vision engineers at tiny firms must manage both of these jobs. They'd have to comb the internet for fresh research papers and emerging methodologies in order to stay on top of things and apply the techniques to the application. It is critical to thoroughly study the computer vision engineer job description in order to fully comprehend what will be expected of you throughout your time at the organization.
The field of computer vision is exploding, and demand for computer vision engineers is at an all-time high. In the United States alone, there are now over 60,000 job openings, and this figure is rapidly growing year after year. Top tech firms such as Apple, Amazon, Facebook, Google, and Rockstar Games are on the lookout for computer vision experts. These figures demonstrate that computer vision engineer positions have a bright future.
In computer vision, state-of-the-art machine learning techniques like Deep Learning, CNN, Tensorflow, Pytorch, and others are being used to conduct extensive research and unique invention. As technologies like machine learning and data science make substantial advances, computer vision will evolve in lockstep. The application of computer vision technology is moving into the public sphere. Computer Vision applications are growing in popularity and will see widespread use in the next years. It's a great moment to learn some cutting-edge computer vision abilities and go ahead in the industry.
The responsibilities of a Computer Vision Engineer include constructing computer vision models, retraining them, producing high-quality datasets, libraries, and reviewing research articles for unique solutions tailored to the product.
Job listings and job descriptions for computer vision are frequently categorized as software engineers by startups and mid-sized businesses. It is a good idea to read over the entire job description as well as the company's expectations. The fact that an engineer would need to participate in software development and engineering chores in addition to computer vision jobs may explain why organizations employ broad titles like software engineer and software developer.
Because each position in computer vision is reliant on the organization and expertise, the job tasks and responsibilities vary. A computer vision engineer's regular day-to-day responsibilities include:
It's worth noting that most major IT organizations have a work division and role-specific tasks. A computer vision engineer will redesign and maintain existing computer vision products that have undergone some development at top tech businesses. A computer vision engineer at a small firm must wear several hats and test existing solutions as well as develop new ones. Startups typically promote comprehensive development and aid in the broadening of abilities in all aspects of computer vision. To understand what the work position comprises, it is critical to read the job description completely.
A full-time degree in computer science or engineering with a specialization in computer vision or advanced machine learning techniques is required of computer vision engineers. The degree might be a master's, bachelor's, or doctoral degree. They should be able to program in an object-oriented manner. In addition to technical skills, applying to jobs with an optimized Computer Vision engineer resume can also help developers to get hired.
A master's, bachelor's, or doctoral degree in computer science engineering or electrical engineering includes computer vision courses.
Certifications and training in computer vision are beneficial, but they are not required for computer vision engineer positions.
The educational requirements listed above are neither all-inclusive nor do they have to be met. Many people in the field who do not have a STEM education but have successfully transitioned into careers as computer vision engineers are outliers. The willingness to study and work hard is the most important prerequisite. The rest is just a bonus.
Become a Turing developer!
While many engineers are unable to shift their backgrounds, new abilities may always be developed. At any stage during your career, you can learn new tools and technologies. It might happen in college, in a new job, or after a 10-year career in any field. Upskilling allows you to make a real impact and advance in any employment capacity. Here's a list of essential computer vision abilities:
Computer vision is a branch of machine learning that heavily relies on deep learning models such as CNN, RNN, and ANN, to mention a few. To identify photos or recognise objects, you'll need to understand machine learning methods.
The most prevalent programming languages used to create computer vision applications are Python, MATLAB, C++, and Cython. Each language has its own set of advantages and drawbacks.
Tensorflow is an open-source machine learning framework that can be used to create and train neural networks for deep learning as well as many other machine learning models that need a lot of numerical calculation. It was created in 2015 by the Google Brain team.
YOLO stands for "You Only Look Once." It is a real-time object detection technique. It detects things in real-time using Convolutional Neural Networks as its foundation.
OpenCV is a free and open-source image processing and computer vision library.
MATLAB is a computer language that allows you to manipulate images as well as do large-scale numerical analyses and displays. It also offers Computer Vision ToolBox, a specific computer vision support tool for building and testing CV systems.
Keras is a python-based open-source framework for implementing deep learning models. It's a wrapper for the Theono and Tensorflow libraries.
Foundational mathematics such as linear algebra, 3D geometry and pattern recognition, fundamental convex optimizations, calculus gradients, and Bayesian Probability is beneficial and desirable.
Object detection uses bounding boxes to recognize things in a picture. It also determines the object's size and placement in the image. Object detection, unlike object localization, is not limited to locating just one instance of an object in an image, but rather all of the object instances present in the image.
The technique of recognizing the single most conspicuous instance of an item in a picture is known as object localization.
The method of monitoring moving objects in a scene or video is commonly utilized in surveillance, CGI movies to track actors, and self-driving automobiles. It employs two methods for detecting and tracking the relevant object(s). The first technique is a generative approach, which looks for regions in a picture that is most comparable to the tracked item while ignoring the backdrop. The discriminative model, on the other hand, looks for contrasts between the item and its surroundings.
Become a Turing developer!
Computer Vision engineers must work hard enough to stay up with the industry's current breakthroughs and to continue to enhance their skills. To thrive, they must effectively and consistently adopt the best practices in their industry. Developers should think about two things as they move forward in this regard. They may seek assistance from someone who is more experienced and good at teaching new skills while practicing. As a Computer Vision engineer, you must also hone your analytical, programming, and soft skills. As a result, the designers must make certain that someone is on hand to help them.
Turing provides the greatest remote Computer Vision chances for experienced Computer Vision Engineers to advance their careers. Working on complex new technological and business issues can help you expand quickly. Join our global developer network to discover long-term, full-time remote Computer Vision engineer jobs with better pay and advancement opportunities.
Long-term opportunities to work for amazing, mission-driven US companies with great compensation.
Work on challenging technical and business problems using cutting-edge technology to accelerate your career growth.
Join a worldwide community of elite software developers.
Turing's commitments are long-term and full-time. As one project draws to a close, our team gets to work identifying the next one for you in a matter of weeks.
Turing allows you to work according to your convenience. We have flexible working hours and you can work for top US firms from the comfort of your home.
Working with top US corporations, Turing developers make more than the standard market pay in most nations.
Every Computer Vision Engineer at Turing can choose their preferred pricing. On the other hand, Turing will propose a salary at which we are certain we can find you a successful and long-term position. Our suggestions are based on our analysis of market conditions as well as customer preferences.