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Careers

Technical Writer

Employment type: Full-time employment
Location: Remote Worldwide or Cyprus
In this role, you'll be essential in breaking down complex technical topics into easy-to-understand language for various audiences, such as users, developers, and the broader community. Your main task will be to develop and keep up-to-date documentation for our computer vision annotation tool, CVAT. You'll ensure that everyone can quickly grasp and use its features.
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Backend Developer

Employment type: Full-time employment
Location: Remote Worldwide or Cyprus
Join us at CVAT.ai, where we're pioneering advancements in Computer Vision. As a Backend Developer, your role is pivotal. You'll architect and develop robust, scalable backend solutions, driving our computer vision data platform forward. This is a chance to work with a skilled team, contribute to open-source projects, and influence the evolution of computer vision technology.
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Frontend Developer

Employment type: Full-time employment
Location: Remote Worldwide or Cyprus
As a Frontend Developer at CVAT.ai, you'll collaborate with our team of developers and engineers to enhance our visual data annotation tool. Your main focus will be on developing the platform and delivering an unparalleled user experience.
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Data Annotator

Employment type: Contract
Location: Remote
As a Data Annotation Specialist at CVAT.ai, you will play an indispensable role in our mission to create comprehensive datasets for Computer Vision applications. Your primary responsibility will involve accurately annotating and labeling data crucial for the training and evaluation of machine learning algorithms, ensuring the precision and reliability of computer vision systems.
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Software Engineer in Test

Employment type: Full-time employment
Location: Remote Worldwide or Cyprus
As a QA Automation Engineer at CVAT.ai, you'll be a pivotal player in our drive to ensure the highest quality in our computer Vision data platform. Your core duty will be to design, develop, and implement automated testing strategies and systems. By doing so, you'll be safeguarding the accuracy and robustness of our machine-learning algorithms, directly contributing to the dependability of our computer vision solutions.
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