What makes you a great fit:

Model Development: Design, develop, and optimize deep learning models for computer vision tasks, including but not limited to object detection, image classification, segmentation, and image generation.

Data Collection & Preprocessing: Collaborate on data acquisition strategies and preprocess large-scale visual datasets (images, videos) to prepare them for model training.

Model Training & Evaluation: Train and fine-tune state-of-the-art models (e.g., CNNs, RNNs, Transformers) on high-performance hardware such as GPUs, ensuring high accuracy and real-time performance.

Optimization: Implement techniques like model quantization, pruning, and compression to optimize models for deployment on various devices, including mobile and edge computing platforms.

Deployment: Deploy computer vision models into production environments and ensure they operate reliably and efficiently in real-time applications.

Research & Innovation: Stay updated with the latest research in computer vision and deep learning, applying innovative techniques to improve model performance and solve complex vision-related challenges.

Collaboration: Work closely with data scientists, software engineers, and product teams to integrate vision models into applications such as augmented reality, autonomous systems, healthcare diagnostics, etc.

Documentation & Reporting: Write technical documentation and reports detailing model architecture, training processes, and deployment pipelines.

Education: Bachelors/Masters/PhD in Computer Science, Electrical Engineering, AI, or a related field.

Proven experience in developing and deploying deep learning models for computer vision tasks.

Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras.

Solid experience with computer vision libraries (e.g., OpenCV) and real-time image processing techniques.

Programming Skills: Proficiency in Python, with experience in C++ or CUDA for GPU-based development.

Mathematics: Strong foundation in linear algebra, probability, and optimization, especially related to vision applications.

Domain Expertise: Deep understanding of convolutional neural networks (CNNs), Generative Adversarial Networks (GANs), vision transformers, and other relevant architectures.

Tools: Experience with image and video processing tools, and familiarity with datasets such as ImageNet, COCO, and custom datasets for specific industries.

Preferred Qualifications: