Computer Vision Expert

About PatSnapPatsnap 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; Ph.D. 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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