Computer Vision and Machine Learning Engineer

Job highlights

• Bachelors or Masters degree in Computer Science,Engineering,or a related field

• Preferred Qualifications: .

• The ideal candidate will have a strong background in image processing,machine learning core concepts,and extensive experience in developing real-time deep learning models for image and video analysis .

• We are seeking a highly skilled Computer Vision and Machine Learning Engineer to join our team

• The ideal candidate will have a strong background in image processing, machine learning core concepts, and extensive experience in developing real-time deep learning models for image and video analysis

• This role requires proficiency in deploying models on edge devices with low latency using optimization libraries like TensorRT

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Responsibilities:

• Research, design, and implement cutting-edge computer vision algorithms and machine learning models for real-time image and video analysis.
• Develop and optimize deep learning models for deployment on edge devices, ensuring low latency and high performance.
• Collaborate with cross-functional teams to understand project requirements and design efficient solutions.
• Explore and experiment with various deep learning architectures and techniques to improve model accuracy and efficiency.
• Collect, preprocess, and analyze image and video data to extract meaningful features for model development.
• Deploy and integrate developed models into production environments, particularly on edge devices.
• Optimize model inference performance on edge devices using libraries such as TensorRT and other optimization techniques.
• Stay up-to-date with the latest advancements in computer vision, machine learning, and edge computing technologies.

Qualifications:

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• Strong background in image processing, computer vision, and machine learning core concepts.
• Extensive experience in developing real-time deep learning models for image and video analysis.
• Proficiency in various deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Knowledge of optimization techniques for deploying models on edge devices, including libraries like TensorRT.
• Familiarity with edge computing concepts and experience in deploying models on edge devices.
• Excellent programming skills in languages such as Python, C/C++, or CUDA.
• Strong analytical and problem-solving skills, with the ability to innovate and adapt to new challenges.
• Effective communication and collaboration skills, with the ability to work in a team environment.

Preferred Qualifications:

• Experience with computer vision applications such as object detection, segmentation, and tracking.
• Knowledge of neural network architectures for image and video processing, including CNNs, RNNs, and GANs.
• Previous experience in optimizing model performance for low-latency inference on edge devices.
• Contributions to open-source projects or publications in relevant conferences or journals