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Lead Multi-Modal AI Research Engineer

2 months ago

California, United States Moonhub Full time

Company Overview: Moonhub is at the forefront of innovation in the Healthtech sector, focusing on developing advanced AI solutions that transform radiology practices.

Position Overview: We are seeking a visionary Founding Multi-Modal Research Engineer with a robust background in vision-language models, video comprehension, and advanced question answering techniques. The ideal candidate will possess extensive experience with state-of-the-art transformer architectures, particularly in their pre-training and fine-tuning on large datasets. Experience in constructing foundational AI models is a significant asset.

Key Responsibilities:

  • Innovative Research: Spearhead groundbreaking research in multi-modal AI, focusing on vision-language models and sophisticated video understanding methodologies.
  • Model Development: Drive innovation with transformer models, emphasizing their pre-training and fine-tuning on extensive datasets.
  • Foundation Model Creation: Create foundational AI models that are versatile and applicable across various tasks within our platform.
  • Data Management: Enhance the utilization of large datasets for training to ensure the development of robust, scalable, and efficient models.
  • Team Collaboration: Collaborate closely with interdisciplinary teams, including data scientists, radiologists, and product managers, to align research with business goals.
  • Thought Leadership: Contribute to the AI community through scholarly publications, patents, and presentations at esteemed conferences.
  • Continuous Advancement: Remain at the cutting edge of AI trends and incorporate leading-edge research into ongoing projects.

Qualifications:

  • Experience: A minimum of 5 years of research experience in AI, with a focus on language or vision models, and at least 1 year specializing in vision-language integration or video comprehension.
  • Expertise: Deep understanding of transformer models, with practical experience in pre-training and fine-tuning large models.
  • Specialization: Strong proficiency in video comprehension, video question answering, and visual question answering.
  • Foundation Models: Demonstrated experience in developing foundational models is highly desirable.
  • Technical Skills: Proficient in Python and deep learning frameworks such as PyTorch or TensorFlow.
  • Research Excellence: A proven track record of high-quality research, evidenced by publications in top-tier conferences or journals.
  • Problem-Solving: Exceptional analytical skills, with the ability to creatively tackle complex research challenges.
  • Communication: Strong verbal and written communication skills, capable of articulating complex concepts to diverse audiences.