Applied Scientist

As a Computer Vision Researcher at our company, your role will involve researching and developing cutting-edge computer vision systems focusing on real-time video analytics, object tracking, and activity recognition. You will work on Vision-Language Models (VLMs) and multimodal transformer architectures to deeply understand visual content. Additionally, you will experiment with self-supervised and few-shot learning techniques to improve model generalization across different video domains. Your contributions will also include exploring LLM prompting strategies and cross-modal alignment methods for enhanced reasoning over vision data. Moreover, you will have the opportunity to contribute to research publications, patents, and internal IP assets within the field of vision and multimodal AI. Key Responsibilities: - Research and build state-of-the-art computer vision systems with an emphasis on real-time video analytics, video summarization, object tracking, and activity recognition. - Develop and apply Vision-Language Models (VLMs) and multimodal transformer architectures for deep semantic understanding of visual content. - Utilize self-supervised, zero-shot, and few-shot learning techniques to enhance model generalization across varied video domains. - Explore and optimize LLM prompting strategies and cross-modal alignment methods for improved reasoning over vision data. - Contribute to research publications, patents, and internal IP assets in the area of vision and multimodal AI. Qualifications Required: - Master's degree in Computer Science, Computer Vision, Machine Learning, or a related discipline with a minimum of 2 years of experience leading applied research or product-focused CV/ML projects. - Proficiency in modern computer vision architectures such as ViT, SAM, CLIP, BLIP, DETR, or similar. - Experience working with Vision-Language Models (VLMs) and multimodal AI systems. - Strong background in real-time video analysis, including event detection, motion analysis, and temporal reasoning. - Familiarity with transformer-based architectures, multimodal embeddings, and LLM-vision integrations. - Proficient in Python and deep learning libraries like PyTorch or TensorFlow, OpenCV. - Experience with cloud platforms (AWS, Azure) and deployment frameworks (ONNX, TensorRT) would be advantageous. - Strong problem-solving skills and a proven track record of end-to-end ownership of applied ML/CV projects. - Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. As a Computer Vision Researcher at our company, your role will involve researching and developing cutting-edge computer vision systems focusing on real-time video analytics, object tracking, and activity recognition. You will work on Vision-Language Models (VLMs) and multimodal transformer architectures to deeply understand visual content. Additionally, you will experiment with self-supervised and few-shot learning techniques to improve model generalization across different video domains. Your contributions will also include exploring LLM prompting strategies and cross-modal alignment methods for enhanced reasoning over vision data. Moreover, you will have the opportunity to contribute to research publications, patents, and internal IP assets within the field of vision and multimodal AI. Key Responsibilities: - Research and build state-of-the-art computer vision systems with an emphasis on real-time video analytics, video summarization, object tracking, and activity recognition. - Develop and apply Vision-Language Models (VLMs) and multimodal transformer architectures for deep semantic understanding of visual content. - Utilize self-supervised, zero-shot, and few-shot learning techniques to enhance model generalization across varied video domains. - Explore and optimize LLM prompting strategies and cross-modal alignment methods for improved reasoning over vision data. - Contribute to research publications, patents, and internal IP assets in the area of vision and multimodal AI. Qualifications Required: - Master's degree in Computer Science, Computer Vision, Machine Learning, or a related discipline with a minimum of 2 years of experience leading applied research or product-focused CV/ML projects. - Proficiency in modern computer vision architectures such as ViT, SAM, CLIP, BLIP, DETR, or similar. - Experience working with Vision-Language Models (VLMs) and multimodal AI systems. - Strong background in real-time video analysis, including event detection, motion analysis, and temporal reasoning. - Familiarity with transformer-based architectures, multimodal embeddings, and LLM-vision integrations. - Proficient in Python and deep learning libraries like PyTorch or TensorFlow, OpenCV. - Experience with cloud platforms (AWS, Azure) and deployment frameworks (ONNX, TensorRT) would be advantageous. - Strong problem-solving skills and a proven track record of end-to-end ownership of applied ML/CV projects