Computer Vision Engineer
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Role details
Tech stack
Job description
- Design, implement, and optimize algorithms for computer vision and machine learning.
- Develop and maintain robust and scalable systems for processing large-scale computer vision systems and image datasets.
- Contribute to the development of novel computer vision techniques for video processing and image recognition.
- Collaborate with hardware engineers to integrate and optimize computer vision pipeline.
Requirements
- Expertise in Computer Vision, Deep Learning and ML algorithms and techniques.
- Strong programming skills in Python; ideally also C++.
- Proficiency in machine learning frameworks (e.g. PyTorch, TensorFlow) and computer vision libraries (e.g. OpenCV).
- Excellent problem-solving and analytical skills.
- Strong communication and teamwork skills.
- 3+ years of experience in production-level computer vision systems.
- Bachelor’s or Master’s in Computer Science, Robotics, or a related field.
Preferred Skills
- Ph.D. in Computer Science, Robotics, or a related field.
- 5+ years of experience in production-grade computer vision systems.
- Experience deploying real-time systems on embedded or robotic platforms.
Benefits & conditions
Be an Early Applicant Remote Hiring Remotely in United States 155K-210K Annually Mid level Remote Hiring Remotely in United States 155K-210K Annually Mid level Develop and optimize computer vision and machine learning algorithms for localization, mapping, detection, video processing, and image recognition. Build scalable systems for large image datasets, collaborate with hardware engineers, and integrate real-time pipelines on embedded or robotic platforms. The role requires production-level computer vision experience and strong Python programming, with C++ preferred. The summary above was generated by AI
About the company
Augmodo is at the forefront of spatial computing, where mapping connects the digital and physical worlds. As a Computer Vision Engineer, you will play a key role in our computer vision team in deploying and developing state-of-the-art algorithms and pipelines for localization, mapping, and detection.
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