Computer Vision Expert

1 month ago


Singapur, Singapore PatSnap Full time
About PatSnap Patsnap empowers IP and R&D teams by providing better answers, so they can make faster decisions with more confidence. Founded in 2007, Patsnap is the global leader in AI-powered IP and R&D intelligence. Our domain-specific LLM, trained on our extensive proprietary innovation data, coupled with Hiro, our AI assistant, delivers actionable insights that increase productivity for IP tasks by 75% and reduce R&D wastage by 25%. IP and R&D teams collaborate better with a user-friendly platform across the entire innovation lifecycle. Over 15,000 companies trust Patsnap to innovate faster with AI, including NASA, Tesla, PayPal, Sanofi, Dow Chemical, and Wilson Sonsini.

Key Responsibilities

Drive innovation and achieve core objectives in OCR, image retrieval, and related computer vision domains. Explore and implement multimodal technologies across existing projects, identifying new applications and opportunities. Lead research and development efforts in advanced computer vision algorithms and techniques. Collaborate with cross-functional teams to integrate computer vision solutions into product offerings.

Desired Qualifications

Master's degree in Computer Science, Software Engineering, Electronic Engineering, Mathematics, Statistics, or a related field; strongly preferred. Minimum of 2 years of hands-on experience in developing and implementing computer vision algorithms. Extensive research and practical experience in text OCR, table OCR, and image retrieval, with a deep understanding of state-of-the-art technologies. Strong foundation in multimodal technologies, including experience with Vision-Language Models (VLMs) and familiarity with multimodal architectures such as CLIP and LLaVA. Demonstrated ability to integrate pre-trained models for document QA, table extraction, image retrieval, and related tasks. Passion for cutting-edge technologies and a track record of successful product delivery. Publications in peer-reviewed journals or top-tier conferences are preferred.

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